research projects in philosophy

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Welcome to PhilPapers

Results of 2020 PhilPapers Survey posted 2021-11-01 by David Bourget We've now released the results of the 2020 PhilPapers Survey, which surveyed 1785 professional philosophers on their views on 100 philosophical issues.  Results are available on the 2020 PhilPapers Survey  website and in draft article form in " Philosophers on Philosophy: The 2020 PhilPapers Survey " . Discussion is welcome in the PhilPapers Survey 2020 discussion group .

Department of Philosophy

Dietrich college of humanities and social sciences, research projects, a new justification for ockham's razor.

Philosophy of science, statistics, and machine learning all recommend the selection of simple theories or models on the basis of empirical data, where simplicity has something to do with minimizing independent entities, principles, causes, or equational coefficients. This intuitive preference for simplicity is called Ockham's razor , after the fourteenth century theologian and logician William of Ockham. But in spite of its intuitive appeal, how could Ockham's razor help one find the true theory when the truth might be complex? This project, involving Kevin Kelly, Conor Mayo-Wilson, and Hanti Lin, concerns the development and extension of a new answer to that question, called the Ockham efficiency theorem . The idea is that it is hopeless to provide an a priori explanation how simplicity points at the true theory immediately, since the truth may depend upon subtle empirical effects that have not yet been observed. But Ockham's razor nonetheless guarantees a priori to keep one on the straightest possible path to the truth, allowing for unavoidable twists and turns along the way as new effects are discovered. The work is currently supported by NSF grant #0740681.

Algebraic Set Theory Resources

Algebraic set theory uses the methods of category theory to study elementary set theory. This website collects together current research in algebraic set theory to promote further development of the subject.

AProS Theorem Prover

AProS ( Automated Pro of S earch) is a theorem prover that aims to find normal natural deduction proofs of theorems in sentential and predicate logic. It does so efficiently for minimal intuitionist and classical versions of first-order logic. The work on AProS is part of the broader educational AProS Project.

The Bernays Project

Paul Bernays was arguably the greatest philosopher of mathematics in the twentieth century, yet much of his work remains untranslated from the original German or French and this is now almost inaccessible to the broader community. This project is very close to completing work on two volumes of a bilingual edition, that is focused on Bernay's paper in philosophy of mathematics.

Causal Judgment in Cognitive and Developmental Psychology

David Danks, Clark Glymour, and Richard Scheines have helped to adapt the causal Bayes net framework to model human learning, an enterprise that is now continued by a number of psychologists across the nation. Danks, Glymour, and Scheines previously participated in the James S. McDonnell Foundation Causal Learning Collaborative that brought together psychologists, philosophers, and computer scientists from multiple institutions to investigate the representations and processes underlying causal learning and judgment in adults and children. That research continues in multiple collaborations that provide opportunities for graduate students and post doctoral fellows.

Causal Learning from Complex Time Series Data

In collaboration with Sergey Plis (Mind Research Network; University of New Mexico), David Danks is exploring our ability to learn causal structure from complex time series data. In particular, they focus on cases in which either the system is measured too slowly (“undersampled” data), or causally important variables are omitted from the time series. These problems are characteristic of neuroimaging, their focal application domain. This research is currently supported by the NSF.

The Causality Lab

This program lets students of statistics and causal reasoning learn about creating and manipulating statistical models. Through its intuitive interface, downloadable lessons teach about interpreting data, methods of discovering causal structure, and other statistical methods. The Causality Lab is incorporated into the Online Learning Initiative's Causal and Statistical Reasoning Course.

The Collected Works of Rudolph Carnap

The Collected Works of Rudolf Carnap will be the first complete edition of Carnap's published philosophical writings. It will be published by the Open Court Publishing Company under the General Editorship of Professor Richard Creath and an editorial board including leading philosophers, logicians and mathematicians.

Computational Systems Biology Group

The Computational Systems Biology Group is an association of statisticians, computer scientists and biologists at Carnegie Mellon University, the University of Pittsburgh and the University of West Florida Institute for Human and Machine Cognition. It investigates statistical, algorithmic, experimental design and biological issues surrounding the interpretation of expression data, especially with SAGE and microarray techniques.

Formal Verification and Automated Reasoning (contact: Jeremy Avigad)

• Interactive Theorem Proving. Potential projects include extending the mathematical library of the  Lean interactive theorem prover , or developing educational materials based on that library. 
• Automated Reasoning. Explore the use of automated theorem provers and constraint solvers in solving mathematical problems.

Full Circle: Publications of the Archive of Scientific Philosophy, Hillman Library, University of Pittsburgh

Under the general editorship of Steve Awodey, Full Circle publishes original, hitherto unpublished documentary material from the Archive of Scientific Philosophy and related collections around the world, as well as monographs and collections of essays on subjects related to papers contained in these archives, spanning the entire history of philosophy, science, and scientific philosophy in the nineteenth and twentieth centuries.

The Hilbert Edition

Hilbert's reputation as one of the greatest mathematicians is well established, yet many of his deepest ideas are found only in lectures that were never formally published. Over the course of six volumes, this project will present the most important of Hilbert's unpublished writings on the foundations of mathematics and of physics. Three of the six volumes have been published by Springer. Of particular significance for the foundations of mathematics are volume I (on geometry) and volumes II and III (on logic and arithmetic). Volumes I and III have been completed, whereas volume II is being prepared now.

Homotopy Type Theory

Homotopy Type Theory refers to a new interpretation of Martin-Löf’s constructive type theory into abstract homotopy theory, which was pioneered at CMU by Awodey and his students. Logical constructions in type theory correspond to homotopy-invariant constructions on spaces, while theorems and even proofs in the logical system inherit a homotopical meaning. Homotopy type theory is closely tied to the new Univalent Foundations of Mathematics program, initiated by Vladimir Voevodsky, for a comprehensive, computational foundation for mathematics based on the homotopical interpretation of type theory. CMU hosts an active research group in this area.

iLogos Argument Mapper

Argument maps are diagrams that display the structure of an argument. By combing pictures and words, argument maps help people create better arguments and analyses. iLogos allows for the easy construction and sharing of argument maps.

Learning Epistemology

This website teaches computational epistemology intuitively using animated and interactive explanations. Developed by Seth Casana, the first lesson available presents Kevin Kelly's research on Ockham's Razor and how it applies to theory choice.

Open Learning Initiative

Through the Open Learning Initiative, Carnegie Mellon offers accessible, high quality online education. It is also an ideal environment to enrich our understanding of effective online teaching techniques. The philosophy department has developed three of the available courses. The first, Logic and Proofs , is an introduction to modern symbolic logic that incorporates the Carnegie Proof Lab. The second, Causal and Statistical Reasoning , is comparable to a full semester course on causal and statistical reasoning taught at Carnegie Mellon University. *The third, Argument Diagramming , is a mini-course introduction to reconstructing and visually representing arguments.

Project PICOLA (Public Informed Citizen Online Assembly)

Under the direction of Robert Cavalier, the department's Digital Media Lab for Applied Ethics and Political Philosophy is collaborating with Jim Fishkin's Center for Deliberative Democracy (Stanford) on developing online tools for Deliberative Polls. Called Project PICOLA, these tools provide the next-generation of computer mediated communication software for online structured dialogue and deliberation.

TETRAD Statistical Modeller

TETRAD is a freeware program for creating and manipulating causal/statistical models. Its intuitive interface and powerful algorithms allow for data prediction and model discovery without the need for cumbersome programming or extensive statistical sophistication.

Watched: Government Surveillance and Privacy in America

Watched delivers an enjoyable online learning experience designed to educate Americans about contemporary threats to personal privacy. It was produced by the students in the philosophy department's Morality Play course in collaboration with a student team from the Entertainment Technology Center. The course explores the potential of interactive media to facilitate values education, and in the spring of 2014, it resulted in this cutting edge educational prototype. Watched will provoke you to both think and care about personal privacy, and open your eyes to the role interactive media can play in educating moral sensibilities.

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Research Opportunities

Research in philosophy is not organized as it is in the sciences, where a single primary investigator oversees and coordinates the work of many different members of a team, from other professors to post-docs to graduate students working on their Ph.D. to undergraduates. Instead, each philosopher's research is fairly self-contained. We share our work in progress with our peers to get feedback and develop our work, of course, but most philosophers don't have researchers doing their own research as part of a larger project.

That means that research opportunities for undergraduates usually take the form of finding funding to spend the summer learning about a topic and writing about it, perhaps in the context of a summer research program or a summer school. Some of these programs are Harvard Internal, some are more broadly based.

Harvard Programs

• SHARP : Summer Humanities and Arts Research Program
• The Harvard Review of Philosophy edits and discusses the articles it considers for publication in its yearly issue. This is a great opportunity to read and discuss philosophy.
• Harvard College Research Funding
• The Mellon Mays Undergraduate Fellowship Program . MMUF exists to counter the underrepresentation of marginalized groups on college and university faculties nationwide through activities designed to encourage the pursuit of the Ph.D. in the humanities and social sciences (from the website).
• Harvard-Cambridge Scholarship summer program. Offers summer scholarships for study in Cambridge, UK.

Other Programs

The programs outside of Harvard are constantly changing, so instead of giving you a list of programs, here are resources that continually update.

• Diversity Institutes : There are various summer programs designed for students from traditionally underrepresented groups.
• Over the course of the academic year, there are round-ups of philosophy summer programs. Daily Nous , a philosophy blog, often has these.

Research Philosophy & Paradigms

Positivism, Interpretivism & Pragmatism, Explained Simply

By: Derek Jansen (MBA) | Reviewer: Eunice Rautenbach (DTech) | June 2023

Research philosophy is one of those things that students tend to either gloss over or become utterly confused by when undertaking formal academic research for the first time. And understandably so – it’s all rather fluffy and conceptual. However, understanding the philosophical underpinnings of your research is genuinely important as it directly impacts how you develop your research methodology.

In this post, we’ll explain what research philosophy is , what the main research paradigms  are and how these play out in the real world, using loads of practical examples . To keep this all as digestible as possible, we are admittedly going to simplify things somewhat and we’re not going to dive into the finer details such as ontology, epistemology and axiology (we’ll save those brain benders for another post!). Nevertheless, this post should set you up with a solid foundational understanding of what research philosophy and research paradigms are, and what they mean for your project.

Overview: Research Philosophy

• What is a research philosophy or paradigm ?
• Positivism 101
• Interpretivism 101
• Pragmatism 101
• Choosing your research philosophy

What is a research philosophy or paradigm?

Research philosophy and research paradigm are terms that tend to be used pretty loosely, even interchangeably. Broadly speaking, they both refer to the set of beliefs, assumptions, and principles that underlie the way you approach your study (whether that’s a dissertation, thesis or any other sort of academic research project).

For example, one philosophical assumption could be that there is an external reality that exists independent of our perceptions (i.e., an objective reality), whereas an alternative assumption could be that reality is constructed by the observer (i.e., a subjective reality). Naturally, these assumptions have quite an impact on how you approach your study (more on this later…).

The research philosophy and research paradigm also encapsulate the nature of the knowledge that you seek to obtain by undertaking your study. In other words, your philosophy reflects what sort of knowledge and insight you believe you can realistically gain by undertaking your research project. For example, you might expect to find a concrete, absolute type of answer to your research question , or you might anticipate that things will turn out to be more nuanced and less directly calculable and measurable . Put another way, it’s about whether you expect “hard”, clean answers or softer, more opaque ones.

So, what’s the difference between research philosophy and paradigm?

Well, it depends on who you ask. Different textbooks will present slightly different definitions, with some saying that philosophy is about the researcher themselves while the paradigm is about the approach to the study . Others will use the two terms interchangeably. And others will say that the research philosophy is the top-level category and paradigms are the pre-packaged combinations of philosophical assumptions and expectations.

To keep things simple in this video, we’ll avoid getting tangled up in the terminology and rather focus on the shared focus of both these terms – that is that they both describe (or at least involve) the set of beliefs, assumptions, and principles that underlie the way you approach your study .

Importantly, your research philosophy and/or paradigm form the foundation of your study . More specifically, they will have a direct influence on your research methodology , including your research design , the data collection and analysis techniques you adopt, and of course, how you interpret your results. So, it’s important to understand the philosophy that underlies your research to ensure that the rest of your methodological decisions are well-aligned .

So, what are the options?

We’ll be straight with you – research philosophy is a rabbit hole (as with anything philosophy-related) and, as a result, there are many different approaches (or paradigms) you can take, each with its own perspective on the nature of reality and knowledge . To keep things simple though, we’ll focus on the “big three”, namely positivism , interpretivism and pragmatism . Understanding these three is a solid starting point and, in many cases, will be all you need.

Paradigm 1: Positivism

When you think positivism, think hard sciences – physics, biology, astronomy, etc. Simply put, positivism is rooted in the belief that knowledge can be obtained through objective observations and measurements . In other words, the positivist philosophy assumes that answers can be found by carefully measuring and analysing data, particularly numerical data .

As a research paradigm, positivism typically manifests in methodologies that make use of quantitative data , and oftentimes (but not always) adopt experimental or quasi-experimental research designs. Quite often, the focus is on causal relationships – in other words, understanding which variables affect other variables, in what way and to what extent. As a result, studies with a positivist research philosophy typically aim for objectivity, generalisability and replicability of findings.

Let’s look at an example of positivism to make things a little more tangible.

Assume you wanted to investigate the relationship between a particular dietary supplement and weight loss. In this case, you could design a randomised controlled trial (RCT) where you assign participants to either a control group (who do not receive the supplement) or an intervention group (who do receive the supplement). With this design in place, you could measure each participant’s weight before and after the study and then use various quantitative analysis methods to assess whether there’s a statistically significant difference in weight loss between the two groups. By doing so, you could infer a causal relationship between the dietary supplement and weight loss, based on objective measurements and rigorous experimental design.

As you can see in this example, the underlying assumptions and beliefs revolve around the viewpoint that knowledge and insight can be obtained through carefully controlling the environment, manipulating variables and analysing the resulting numerical data . Therefore, this sort of study would adopt a positivistic research philosophy. This is quite common for studies within the hard sciences – so much so that research philosophy is often just assumed to be positivistic and there’s no discussion of it within the methodology section of a dissertation or thesis.

Paradigm 2: Interpretivism

 If you can imagine a spectrum of research paradigms, interpretivism would sit more or less on the opposite side of the spectrum from positivism. Essentially, interpretivism takes the position that reality is socially constructed . In other words, that reality is subjective , and is constructed by the observer through their experience of it , rather than being independent of the observer (which, if you recall, is what positivism assumes).

The interpretivist paradigm typically underlies studies where the research aims involve attempting to understand the meanings and interpretations that people assign to their experiences. An interpretivistic philosophy also typically manifests in the adoption of a qualitative methodology , relying on data collection methods such as interviews , observations , and textual analysis . These types of studies commonly explore complex social phenomena and individual perspectives, which are naturally more subjective and nuanced.

Let’s look at an example of the interpretivist approach in action:

Assume that you’re interested in understanding the experiences of individuals suffering from chronic pain. In this case, you might conduct in-depth interviews with a group of participants and ask open-ended questions about their pain, its impact on their lives, coping strategies, and their overall experience and perceptions of living with pain. You would then transcribe those interviews and analyse the transcripts, using thematic analysis to identify recurring themes and patterns. Based on that analysis, you’d be able to better understand the experiences of these individuals, thereby satisfying your original research aim.

As you can see in this example, the underlying assumptions and beliefs revolve around the viewpoint that insight can be obtained through engaging in conversation with and exploring the subjective experiences of people (as opposed to collecting numerical data and trying to measure and calculate it). Therefore, this sort of study would adopt an interpretivistic research philosophy. Ultimately, if you’re looking to understand people’s lived experiences , you have to operate on the assumption that knowledge can be generated by exploring people’s viewpoints, as subjective as they may be.

Paradigm 3: Pragmatism

Now that we’ve looked at the two opposing ends of the research philosophy spectrum – positivism and interpretivism, you can probably see that both of the positions have their merits , and that they both function as tools for different jobs . More specifically, they lend themselves to different types of research aims, objectives and research questions . But what happens when your study doesn’t fall into a clear-cut category and involves exploring both “hard” and “soft” phenomena? Enter pragmatism…

As the name suggests, pragmatism takes a more practical and flexible approach, focusing on the usefulness and applicability of research findings , rather than an all-or-nothing, mutually exclusive philosophical position. This allows you, as the researcher, to explore research aims that cross philosophical boundaries, using different perspectives for different aspects of the study .

With a pragmatic research paradigm, both quantitative and qualitative methods can play a part, depending on the research questions and the context of the study. This often manifests in studies that adopt a mixed-method approach , utilising a combination of different data types and analysis methods. Ultimately, the pragmatist adopts a problem-solving mindset , seeking practical ways to achieve diverse research aims.

Let’s look at an example of pragmatism in action:

Imagine that you want to investigate the effectiveness of a new teaching method in improving student learning outcomes. In this case, you might adopt a mixed-methods approach, which makes use of both quantitative and qualitative data collection and analysis techniques. One part of your project could involve comparing standardised test results from an intervention group (students that received the new teaching method) and a control group (students that received the traditional teaching method). Additionally, you might conduct in-person interviews with a smaller group of students from both groups, to gather qualitative data on their perceptions and preferences regarding the respective teaching methods.

As you can see in this example, the pragmatist’s approach can incorporate both quantitative and qualitative data . This allows the researcher to develop a more holistic, comprehensive understanding of the teaching method’s efficacy and practical implications, with a synthesis of both types of data . Naturally, this type of insight is incredibly valuable in this case, as it’s essential to understand not just the impact of the teaching method on test results, but also on the students themselves!

Wrapping Up: Philosophies & Paradigms

Now that we’ve unpacked the “big three” research philosophies or paradigms – positivism, interpretivism and pragmatism, hopefully, you can see that research philosophy underlies all of the methodological decisions you’ll make in your study. In many ways, it’s less a case of you choosing your research philosophy and more a case of it choosing you (or at least, being revealed to you), based on the nature of your research aims and research questions .

• Research philosophies and paradigms encapsulate the set of beliefs, assumptions, and principles that guide the way you, as the researcher, approach your study and develop your methodology.
• Positivism is rooted in the belief that reality is independent of the observer, and consequently, that knowledge can be obtained through objective observations and measurements.
• Interpretivism takes the (opposing) position that reality is subjectively constructed by the observer through their experience of it, rather than being an independent thing.
• Pragmatism attempts to find a middle ground, focusing on the usefulness and applicability of research findings, rather than an all-or-nothing, mutually exclusive philosophical position.

If you’d like to learn more about research philosophy, research paradigms and research methodology more generally, be sure to check out the rest of the Grad Coach blog . Alternatively, if you’d like hands-on help with your research, consider our private coaching service , where we guide you through each stage of the research journey, step by step.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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13 Comments

was very useful for me, I had no idea what a philosophy is, and what type of philosophy of my study. thank you

Thanks for this explanation, is so good for me

You contributed much to my master thesis development and I wish to have again your support for PhD program through research.

the way of you explanation very good keep it up/continuous just like this

Very precise stuff. It has been of great use to me. It has greatly helped me to sharpen my PhD research project!

Very clear and very helpful explanation above. I have clearly understand the explanation.

Very clear and useful. Thanks

Thanks so much for your insightful explanations of the research philosophies that confuse me

I would like to thank Grad Coach TV or Youtube organizers and presenters. Since then, I have been able to learn a lot by finding very informative posts from them.

thank you so much for this valuable and explicit explanation,cheers

Hey, at last i have gained insight on which philosophy to use as i had little understanding on their applicability to my current research. Thanks

Tremendously useful

thank you and God bless you. This was very helpful, I had no understanding before this.

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Department of Philosophy

Research projects.

REF 2021: Reducing Coercion in Psychiatric Care

The impact case study bears evidence of the contribution philosophical thinking can provide to improve psychiatric practice.

REF 2021: Managing Knowledge of Genetic Relatedness in Donor Conception

What kind of information should donor-conceived people be able to find out about their donor and donor-conceived siblings?

How Does it Feel? Interpersonal Understanding and Affective Empathy

Professor Thomas Schramme has been awarded £350,000 for the project.

Life, Death and Meaning

Can we see the value in things only if they are fragile and bound to perish? Why should it be necessary for life to have not only a beginning, but also an end? Would that imply that for the world as a whole to have meaning, it too will have to end one day? And why should we need a final purpose, instead of an open-ended sequence of purposes to find meaning in life?

Women In Parenthesis

Women In Parenthesis explores the contribution of a remarkable group of philosophers who met as young women at Oxford University during World War II.

Philosophy and the Future

Our pioneering Philosophy and the Future research theme focuses on the ethical, political, social, metaphysical and spiritual implications of climate change, technological and scientific developments, and emerging forms of social and political interaction.

Philosophy in the World

The Department of Philosophy has a long and distinguished history of combining research excellence in core areas of philosophy with innovative work at the frontiers of the discipline as this intersects with matters of wider public concern.

Olaf Stapledon Centre for Speculative Futures

The Olaf Stapledon Centre for Speculative Futures is an interdisciplinary research centre based in the University of Liverpool, bringing together researchers from various disciplines but focused primarily on English, Philosophy, and Special Collections & Archives.

Entrapment, Criminal Justice, and Ethics

What makes an act one of entrapment? Is entrapment morally permissible? If so, under what constraints? What are entrapment’s normative implications for practical ethics and for criminal justice?

Normative Implications of the Metaphysics of Extra-Corporeal Gestation

This project will use the current philosophical debate about the metaphysics of pregnancy, and technological developments in reproductive sciences, as springboards for discussing the normative consequences of how we fundamentally conceive of the relation between foster and gravida.

Past projects

• Current Philosophy Students
• Current Philosophy Staff

Department of Philosophy Mulberry Court, Mulberry Street University of Liverpool, Liverpool L69 7ZY +44 (0)151 795 0500

Research projects

Blame and responsibility.

The project's core objective is to bring together researchers in epistemology, ethics and metaphysics to shed light on key questions concerning blame including the following:

• What is it to blame someone and under what conditions are we entitled to do so?
• Are groups, such as companies or governments, blameworthy?
• To what extent can groups or individuals be held blameworthy for beliefs or behaviour which discriminate against women or minorities even when the beliefs and behaviour are subconscious or accepted practice?
• What can be revealed about blame by the nature of forgiveness and apology?

Visit the  Blame and Responsibility website .

Effective Altruism

Effective altruism is a philosophy and social movement focusing on getting the most good out of charitable activities (donations of money, time, and effort). It has received popular attention, but a number of philosophical issues surrounding it remain under-explored. For example:

• What is the best statement of effective altruism as a philosophical view, and what is its relation to consequentialism, deontology, or virtue ethics? Is the view tenable?
• What is the most important cause? Fighting extreme poverty, reducing existential risks, or what? To what extent, if at all, should we take into consideration the well-being of possible future persons? How should we decide where to give if there is no clear best cause?
• To what extent is progress in ethical theory a priority, from an effective altruist perspective? For example, how important is it to figure out what well-being consists in, or to solve problems in population ethics?

Visit the Effective Altruism website .

The Logic of Conceivability

The Logic of Conceivability (LoC) is a five-year project (2017-2021) funded by the European Research Council. It is co-hosted by St Andrews and by the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam.

We often wonder what would happen if this or that possibility were realised: "What if Brexit causes a recession? Will I lose my job?". "How will the electorate react if my political party lowers taxes?". Reasoning about such hypothetical scenarios is of fundamental importance to plan our future and make choices. But how do we reason when we imagine such scenarios? What is the logic of our imagination?

The orthodox logical treatment of mental states like believing, imagining, supposing, or knowing, comes from modal logic’s possible worlds semantics, which was taken up by philosophy, linguistics, and artificial intelligence. However, the approach faces major problems. For example it models heavily idealised, logically infallible reasoners. It is, thus, disconnected from the reality of human, fallible minds. LoC addresses them via the techniques of non-classical logics and non-normal worlds semantics.

The LoC group includes the principal investigator, Franz Berto, and  a team of mathematical logicians, epistemologists, and psychologists of reasoning . The project outputs are all available through open access .

Philosophy, Philosophizing and the Philosopher in 18th-Century Britain

Professor James Harris is working on a new history of 18th-century British philosophy. This project is funded by a senior research fellowship from the British Academy and the Leverhulme Trust in 2020.

Harris' interest is in significant but not yet properly defined differences between philosophy as it was practised in 18th-century Britain and philosophy as it is practised now. These differences tend to be ignored in standard history of philosophy, probably because such history tends to be written by philosophers who are disposed to focus on similarities and continuities between the philosophy of the past and the philosophy of the present. Professor Harris believes that historical investigation is needed to determine what, exactly, philosophy was in Britain in the 18th century.

• What were its goals?
• Who did it, and why?
• How was it done?
• What was its social role?

Theories of Paradox in 14th-Century Logic: Edition and Translation of Key Texts

The project consists of preparing an edition of the Latin text, together with an English translation and commentary, of the late 14th-century treatise on Insolubles (logical paradoxes) by Paul of Venice from his Logica Magna .

The main aim is to provide scholars and students with access to important and interesting texts from the 14th century on the logical paradoxes.

In the final stage of the project the team will edit and translate two further treatises by Walter Segrave and John Dumbleton, which were written in Oxford in the second quarter of the century, and which Paul mentions. They remain unedited, containing rich ideas about alternative solutions, restrictio and cassatio respectively. Publication of these texts will allow a better overview of the development of solutions to the paradoxes through the 14th century, as well as give further insight into the nature of the paradoxes and their possible solution.

The project is funded by a Leverhulme Research Project grant to Professor Stephen Read, funding a Research Fellowship held by Dr Barbara Bartocci. It began on 1 August 2017 and continues until 31 July 2020.

Visit the  Theories of Paradox website .

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Research projects

• Research Areas
• Dissertations
• Research groups
• Knowledge in Crisis
• Epistemology of the In/Human
• The Quantified Argument Calculus
• Modal Reasoning, Quarc and Metaphysics
• Past projects
• Research & Publish Lab

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In This Section

• Faculty Bookshelf: A – E
• Faculty Bookshelf: F – J
• Faculty Bookshelf: K – O
• Faculty Bookshelf: P – T
• Faculty Bookshelf: U – Z
• Research Interest Groups
• Balzan Research Project (2015-19) – Styles of Reasoning
• Major Research Collaborations
• Ergo: An Open-Access Journal of Philosophy

The Department of Philosophy is home to a variety of research projects and collaborative groups which advance important and wide-ranging work in all areas of philosophy. Departmental research elicits contributions from faculty, graduate students, and undergraduate students alike.

Explore our research projects, groups, publications, and collaborations.

Undergraduate Research Opportunities

Philosophy, Religion, and PPE students can participate in academic research in many ways: taking classes focused on research practices and methodologies, working on faculty-led research projects, developing independent research projects, or writing honors theses.

Research-Intensive Classes and Independent Research Projects. Research experience is an integral part of the majors in Philosophy, Religious Studies, and Politics, Philosophy and Economics (PPE). And while students in the major engage in a variety of research experiences throughout their classes, the department also offers several research-intensive courses.

• Directed Study, PHIL 4992. Can be done for 1-4 credits. Students study a topic with a faculty advisor, and can include working with the faculty on research on the topic.
• Research Practicum, PHIL 2991. Can be done for 2-4 credits. Students do collaborative work with a faculty member with the goal of teaching them research methods.
• Honors in the Major, CSSH/Department, independent student projects with faculty advisors. Department guidance on honors in the major is under the "Academics" tab on our website.
• Research Activity through INSH 2992, which is a 0 credit hour course/no tuition but shows up on student transcript as research activity.

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Philosophy of Research: An Introduction

• First Online: 30 August 2023

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• Santosh Kumar Yadav 2  

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The word research itself is a combination of “ re ” and “ search ,” which is meant by a systematic investigation to gain new knowledge from already existing facts. Frankly speaking, research may be defined as a scientific understanding of existing knowledge and deriving new knowledge to be applied for the betterment of the mankind. In the words of Wernher von Braun (a German philosopher), “ Research is what I’m doing when I don’t know what I’m doing .” It is basically the search for truth/facts. The significant contribution of research deals with the progress of the nation as well as an individual with commercial, social, and educational advantages. Albert Szent Gyorgyi (Hungarian Biochemist, Nobel Prize 1937) writes “ Research is to see what everybody else has seen and think what nobody has thought .” Research may be an important parameter to judge the development of any nation/generation. According to Clifford Woody (American philosopher, 1939), “ Research comprises of defining and redefining problems, formulating the hypothesis for suggested solutions, collecting, organizing and evaluating data, making deductions and reaching conclusion and further testing the conclusion whether they fit into formulating the hypothesis .” The major objectives of research are to find out a hidden and undiscovered truth of the nature/society. There are various objectives behind undertaking research by individuals as well as various organizations/universities. Some philosophical objectives behind any research include:

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Further Reading

Bairagi V, Munot MV (2019) Research methodology. A practical and scientific approach. CRC Press Taylor & Francis Group, New York, NY

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Brandenburg R, McDonough S (2019) Ethics, self-study research methodology and teacher education. Springer Nature Singapore Pte Ltd., Cham

Bridges D (2017) Philosophy in educational research: epistemology, ethics, politics and quality. Springer International Publishing AG, Cham

Chawla D, Sondhi N (2015) Research methodology: concepts and cases. Vikas® Publishing House Pvt Ltd, New Delhi

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Jain S (2019) Research methodology in arts, science and humanities. Society Publishing, Oakville, ON

Kothari CR (2004) Research methodology: methods and techniques. New Age International (P) Ltd, New Delhi

Kumar R (2011) Research methodology: a step-by-step guide for beginners. SAGE Publications India Pvt Ltd, New Delhi

Novikov AM, Novikov DA (2013) Research methodology: from philosophy of science to research design. CRC Press Taylor & Francis Group, Boca Raton, FL

Pring R (2000) Philosophy of educational research. Continuum, London

Pruzan P (2016) Research methodology: the aims, practices and ethics of science. Springer International Publishing Switzerland, Cham

Smeyers P, Depaepe M (2018) Educational research: ethics, social justice, and funding dynamics. Springer International Publishing AG, (part of Springer Nature), Cham

Yadav SK (2015) Elements of research writing. UDH Publishers and Distributers, New Delhi

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Review Questions

Explain the term “research” in view of two philosophers.

Briefly describe the different steps involved in a research process.

Describe the different types of research, clearly pointing out the difference between an experiment and a survey.

“Empirical research in India in particular creates so many problems for the researchers.” State the problems that are usually faced by such researchers.

“Creative management, whether in public administration or private industry, depends on methods of inquiry that maintain objectivity, clarity, accuracy, and consistency.” Discuss this statement and examine the significance of research.

What is research problem? Define the main issues which should receive the attention of the researcher in formulating the research problem. Give suitable examples to elucidate your points.

“Knowing what data are available often serves to narrow down the problem itself as well as the technique that might be used.” Explain the underlying idea in this statement in the context of defining a research problem.

What is research design? Discuss the basis of stratification to be employed in sampling public opinion on inflation.

Give your understanding of a good research design. Is single research design suitable in all research studies? If not, why?

“It is never safe to take published statistics at their face value without knowing their meaning and limitations.” Elucidate this statement by enumerating and explaining the various points which you would consider before using any published data. Illustrate your answer by examples wherever possible.

“Experimental method of research is not suitable in management field.” Discuss, what are the problems in the introduction of this research design in business organization?

What are common features of good research?

How many ways the philosophical scientific knowledge may be classified in the research?

Explain Wilfred Carr’s concept of educational philosophy and theory.

What is the difference between philosophy of research and philosophy in research?

What is the physical relationship between learning and experience?

What is the impact of action research on the scholar’s learning?

What are philosophical features of a good research study?

Explain the role of a philosopher in an interdisciplinary research.

Write Short Notes on the following:

Educational Research and Philosophy

Philosopher in the Classroom

Educational Research: Pursuit of Truth

Interdisciplinary Research

Pragmatic Theory of Knowledge

Ethical Codes and Academic Independence

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• Published: 31 January 2024

Computational philosophy: reflections on the PolyGraphs project

• Brian Ball   ORCID: orcid.org/0000-0003-2478-6151 1 ,
• Alexandros Koliousis 1 ,
• Amil Mohanan   ORCID: orcid.org/0000-0001-8408-7198 1 &
• Mike Peacey 2  

Humanities and Social Sciences Communications volume  11 , Article number:  186 ( 2024 ) Cite this article

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In this paper, we situate our computational approach to philosophy relative to other digital humanities and computational social science practices, based on reflections stemming from our research on the PolyGraphs project in social epistemology. We begin by describing PolyGraphs. An interdisciplinary project funded by the Academies (BA, RS, and RAEng) and the Leverhulme Trust, it uses philosophical simulations (Mayo-Wilson and Zollman, 2021 ) to study how ignorance prevails in networks of inquiring rational agents. We deploy models developed in economics (Bala and Goyal, 1998 ), and refined in philosophy (O’Connor and Weatherall, 2018 ; Zollman, 2007 ), to simulate communities of agents engaged in inquiry, who generate evidence relevant to the topic of their investigation and share it with their neighbors, updating their beliefs on the evidence available to them. We report some novel results surrounding the prevalence of ignorance in such networks. In the second part of the paper, we compare our own to other related academic practices. We begin by noting that, in digital humanities projects of certain types, the computational component does not appear to directly support the humanities research itself; rather, the digital and the humanities are simply grafted together, not fully intertwined and integrated. PolyGraphs is notably different: the computational work directly supports the investigation of the primary research questions, which themselves belong decidedly within the humanities in general, and philosophy in particular. This suggests an affinity with certain projects in the computational social sciences. But despite these real similarities, there are differences once again: the computational philosophy we practice aims not so much at description and prediction as at answering the normative and interpretive questions that are distinctive of humanities research.

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Introduction

Philosophy and computing have long and inter-related histories: for instance, the formal investigation of logic was initiated by the philosopher Aristotle over two millennia ago; and it was developments in this field in the late 19th and early 20th centuries that led quite directly to Turing’s work, and the invention of the modern electronic computer (Ball and Koliousis, 2022 ). Nevertheless, while ‘humanities computing’ (McCarty, 2003 ) became common within the academy in the early years of the 21st century, philosophers have arguably failed to take full advantage of the opportunities afforded. Why? As Berry ( 2012 ) reports, the computing work in such endeavors has often been ‘seen as [merely] a technical support to the work of the ‘real’ humanities scholars’ (2012: p. 2). One hypothesis, then, is that philosophers are (or have been) particularly inclined to adopt such a view.

We will not assess this sociological conjecture here—after all, considerable empirical evidence that we do not possess would be required to either confirm or disconfirm it—but we will engage with the objection (concerning the role of computing in relation to the humanities) that underlies it. More specifically, in this paper, we describe a project we are pursuing in computational philosophy (Grim and Singer, 2022 ) about which the above complaint cannot be raised: the computational and humanities components of our project are thoroughly intertwined; and accordingly, there is no plausibility to the claim that the former play a merely supporting (i.e., non-intellectual) role.

We begin by outlining our project and reporting some of its initial findings. We then compare our approach to those pursued in other projects in the digital humanities and computational social sciences. In this way, we aim to situate the PolyGraphs project’s computational philosophy within the intellectual landscape.

Our project is entitled ‘PolyGraphs: Combatting Networks of Ignorance in the Misinformation Age’. Funded for two years by the British Academy, the Royal Society, the Royal Academy of Engineering, and the Leverhulme Trust under the APEX award scheme, it brings together researchers from a range of disciplines (philosophy, computer and data science, economics) in order to explore information flow in social networks, and the concomitant dynamics of knowledge and ignorance in communities of inquiring agents. The topic is timely, as online misinformation (or even disinformation) about, e.g., coronavirus or the climate emergency can result in ignorance and polarization, preventing effective individual and collective action; and it might be hoped that investigations in this area will influence government and/or corporate policy to combat such pressing practical problems.

Nevertheless, it is worth stressing that our research ultimately has a broader scope than these particular applications suggest. To begin with, there is nothing in our approach that restricts attention to online communities: at its heart, PolyGraphs is a project in social epistemology (Goldman, 1999 ); and as such it concerns knowledge and ignorance in social contexts more generally, not just those that are technologically (let alone computationally and/or digitally) enhanced. Indeed, our guiding research question may be roughly formulated as follows:

(Q) How ought we rationally to form opinions (i.e., beliefs), both individually and collectively?

This question is simultaneous: (i) normative —it asks not how things are , but how they ought to be; and (ii) interpretive —it requires us to consider how best to understand (e.g.) the notion of rational belief. We will return to these points below.

Also noteworthy for some readers may be our assumption that there are facts of the matter about what the correct answers are to the questions under investigation in the communities that interest us. While opinions may (reasonably) differ, we assume that some are ultimately correct, while others are erroneous. For example, vaccines are effective against coronavirus; and climate change really is caused by the consumption of fossil fuels—even if there are considerable bodies of (unscientific) opinion to the contrary. This is not to deny that social factors influence which opinions are adopted within a given community—indeed, our investigation explores precisely such influences. Nevertheless, knowledge entails truth—and so belief in a falsehood, whatever the cause, does nothing to alleviate ignorance.

Finally, and relatedly, note that we might seek to explain ignorance within our target communities by appealing to various irrationalities, including psychological ‘heuristics’ (Kahneman, 2011 ) that are deployed in everyday information processing and that depart from the ideal, or outright ‘intellectual vices’ (Cassam, 2018 ) that infect even conscious reasoning. Instead, we explore the possibility that such ignorance can be (at least partially) explained in terms of the social structures in which individuals are embedded. Even if our approach is somewhat unrealistic in assuming the rationality of the individuals that constitute our target communities, the idealization it involves has two virtues: first, it provides an opportunity to determine whether ignorance can arise, or persist, through no rational fault of the individuals involved; and second, it allows us to address our overarching research question (Q), given above, by exploring the effects of treating various different strategies as candidates for rationality.

The Zollman effect

Our approach involves first modeling, and then simulating, the social processes of opinion formation that interest us. Our basic model derives from economics (Bala and Goyal, 1998 ): rational agents conduct experiments to obtain new evidence; they share this evidence with their neighbors in the social network to which they belong; and they update their beliefs on the matter under investigation in light of the totality of the evidence at their disposal—including that which is provided by their neighbors. Following others (see below), we conduct philosophical simulations (Mayo-Wilson and Zollman, 2021 ) based on these models to see how inquiring communities of rational agents behave over time.

Zollman ( 2007 ) was the first philosopher to build simulations of the kind we employ. He imagined a community of scientists researching a particular disease, and testing which of two treatments, A or B , is more effective in combating it. It is known in this community that treatment A is effective with a probability of 0.5. Treatment B , however, is effective with probability 0.5 +  ϵ , and the agents need to determine whether ϵ is positive or negative in order to determine whether treatment B is better than treatment A . In fact, ϵ is positive in the models in question, and B is better (in this sense).

The individual scientists in this community are modeled as having a degree of belief, or credence, between 0 and 1 in the proposition that B is better, initially assigned at random from a uniform distribution. Those whose credence is above 0.5 are treated as believing that B is better; they accordingly administer treatment B to their n patients—and in so doing conduct an experiment that provides evidence of the effectiveness of treatment B . In particular, they are able to observe how many of their n patients recover. (It is assumed that recovery is an all-or-nothing affair.). Scientists who think (falsely) that A is better—that is, those whose credence that B is better is below 0.5—administer treatment A ; but as its effectiveness is known, this generates no new relevant evidence about the relative merits of A and B .

The community (of scientists working on this disease) as a whole is modeled as a graph, comprising (a set of) nodes and edges connecting them. The scientists at the nodes share their findings (if any) with those to whom they are connected. They then update their credences in light of the evidence at their disposal—this comprises their own findings, as well as the findings of those who are connected to them. Updating is performed using Bayes’ rule:

In other words, the final (or posterior) probability function after updating on the evidence e assigns to a hypothesis h the initial conditional probability of that hypothesis on that evidence—which in turn is related to the other initial quantities as described (by Bayes’ theorem). Footnote 1

The entire process described above of performing an experiment (or not, for A believers), informing neighbors of the results (if any), and updating beliefs accordingly, constitutes a single simulation step. It is repeated until either all agents believe that A is better, and so generate no further evidence, or they all have credence above 0.99 in the proposition that B is better, making it exceedingly unlikely that they will go on to change their minds. Footnote 2

Zollman generated his graphs artificially, subject to certain constraints. For example, in some simulations, he specified that the community of scientists should form a ‘complete’ network, with every node connected to every other by an edge. In others, he stipulated that each scientist should be connected to precisely two neighbors, with the first and last scientists in the network connected to one another as well, and the community as a whole, therefore, constituting a (ring or) ‘cycle’. Footnote 3 What he found was that: first, more sparsely connected networks such as the cycle are more reliable in converging to the true belief that B is better than more densely connected ones; and second, more densely connected networks are faster at converging to the truth (i.e., they do so in fewer steps), so that there is a tradeoff between speed and accuracy/reliability. Footnote 4

Comparing polarization models

O’Connor and Weatherall ( 2018 ) adapted Zollman’s approach to accommodate scenarios under which it might be rational to distrust evidence provided by others. In their simulations, scientists update their beliefs using Jeffrey’s rule:

When the final probability of the evidence is equal to 1, this is equivalent to Bayes’ rule; but in general, it allows uncertain evidence e to be discounted, with some weight given to the alternative possibility that ¬  e . Of course, the amount of discounting applied to a given piece of evidence must be determined somehow—this is not set by the rule itself. O’Connor and Weatherall explore the idea that agents trust others more when they are more alike, and in particular when the absolute difference (or distance d ) between their credences is smaller. More specifically still, they run simulations in which the final probability of the evidence e provided by a neighbor is set by the formula:

Here the idea is that evidence is completely believed when it is supplied by someone who has the exact same credence as the agent does (e.g., the agent herself ‘reporting’ her own experimental findings)—and when the product of the distance between beliefs and the ‘mistrust multiplier’ m (which serves to amplify the effect of this distance) reaches (and then exceeds, but is replaced by) 1, the new evidence is completely ignored, having no effect on the final credence, leaving it exactly as it was. In between these extremes, the evidence e receives some boost in the agent’s credence, but it is not treated as certain. Footnote 5

O’Connor and Weatherall note that, when updating is done as indicated, polarization is a possible outcome: that is, a simulation can end up with some agents having credence above 0.99, while all others have credence below 0.5, yet no further evidence produced by the former will convince the latter to change their mind, since it is completely discounted (i.e., ignored) and P f ( e ) =  P i ( e ). ‘In our models,’ they report, ‘over all parameter values, we found that only 10% of trials ended in false consensus, 40% in true consensus, and 50% in polarization.’ (2018: 866) Unfortunately, this aggregate report (‘over all parameter values’) does not allow us to directly compare the prevalence of ignorance in Zollman’s Bayesian models with O’Connor and Weatherall’s polarization models in which Jeffrey’s rule is employed.

As part of our PolyGraphs project, we built the Python code needed, and ran simulations on complete networks, using both Zollman and polarization models. We then compared: (i) the proportion of simulations (of a given size, and with a given ϵ value) that arrived at the consensus that B is better; and (ii) the number of steps needed to arrive at that consensus in those simulations that did so. We found that, comparing like for like, the models allowing polarization (i.e., those with mistrust multiplier m  > 1) resulted in a lower proportion reaching consensus in the truth (i.e., more ignorance Footnote 6 ), and an increase in the number of steps required to do so. Table 1 (below), for instance, shows the percentage of simulations converging to the correct consensus that B is better in relatively small (complete) networks (of size 16 and 64), and with relatively small values of ϵ (0.001 and 0.01), where the Zollman effect was known to occur. As can be seen, polarization models converged to the truth in a smaller percentage of cases, with this effect being more pronounced for larger (values of the mistrust multiplier) m . In short, the more that agents in our simulations distrusted others based on their divergent beliefs, the more ignorance resulted.

As for the number of steps required to arrive at the correct consensus (that B is better) in those that did so, we again found that, when comparing simulations with the same parameter values, ignorance persisted for longer, on average, in the O’Connor and Weatherall models than in the Zollman models. In particular, the number of steps required to achieve an accurate consensus was significantly ( p  < 0.05) greater in (small, low ϵ value) simulations based on the former models than in the latter, whether the mistrust multiplier was 1.1 or 1.5. In short, ignorance took much longer to eradicate in our simulations when agents discounted the (reliable) evidence provided by their peers (Table 2 ).

Group belief

We have seen that, for O’Connor and Weatherall, polarization is regarded as arising whenever there is a stable departure from consensus. In other words, when all agents’ beliefs are stable, the community is polarized (on their account) provided at least one believes that A (has credence < 0.5) and one believes that B (has credence > 0.99). We assume that a group of agents cannot be said to believe something if it is polarized on the issue at hand: and, of course, belief is necessary for knowledge; so we (informally) classed simulations ending in polarization as ones involving ignorance on the part of the community.

The definition of polarization, however, could be strengthened—and the requirements on group belief Footnote 7 concomitantly weakened. Thus, whereas O’Connor and Weatherall effectively require consensus before they are willing to say that the community believes that B is better, we might consider other accounts of group belief: for instance, it might be thought that a group believes something provided a simple majority of its members do; or provided a supermajority (of e.g., two-thirds, or three-fifths) does. In fact, we are interested in the possibility that whether a group believes something depends not only on how many of its members do so but also on how the members are related to one another—that is, on group structure. Accordingly, we wish to compare methods of aggregating individual beliefs into a group belief that is sensitive or insensitive to structure.

It is worth noting that the effects of structure sensitivity are difficult to discern (if they exist) in the kind of small, artificial networks that have so far been our focus. Accordingly, our code is devised in such a way as to allow us to scale our simulations—and we can import large-scale, real-world networks to base them on. We ran our code on one such imported real-world network—though admittedly, EgoFacebook (Leskovec and Mcauley, 2012 ) is relatively modest, at approximately 4000 nodes. Footnote 8 In Fig. 1 , we analyze the results from a simulation on this network over 100, 000 steps, looking at what size of majority (i.e., what proportion) of nodes in the network had credence above 0.99 every 1000 steps. In the ’unweighted beliefs’ plot, ‘voting’ is unweighted, so that all nodes count equally. (This is a structure-insensitive aggregation technique.) In the second ‘weighted beliefs’ plot, the number of votes a node receives is weighted by the size of its neighborhood (i.e., the aggregation method is structure-sensitive in this way). As can be readily seen, the size of the ‘majority’ increases much more quickly when voting is weighted (reflecting the underlying fact that nodes with larger neighborhoods are reaching a credence of 0.99 more quickly than others are). Thus, if (for example) a three-quarters supermajority of votes is required for group belief, this is achieved (and group ignorance avoided) in less than 10,000 steps with weighting. It is not achieved in the first 20,000 steps without. And, of course, consensus is not achieved for tens of thousands more steps. In short, the aggregation technique matters when it comes to assessing group attitudes—and structure sensitivity in particular makes a difference.

Votes are either unweighted (i.e., one node, one vote), or weighted to give each node a number of votes equal to its neighborhood size.

Of course, other structure-sensitive aggregation methods are possible. But is structure sensitivity itself appropriate? In our view, it may well be. Beliefs enter into relations of two kinds—rational and causal. But when edges are undirected (as in EgoFacebook) nodes with larger neighborhoods are both causally more influential (affecting more neighbors) and rationally sensitive to more evidence (from more neighbors)—and their beliefs are therefore arguably more representative of the belief of the network as a whole. In future work, we will disentangle these two elements (causal influence and rational authority), exploring a range of structure-sensitive measures of group belief on large directed graphs.

We conclude this first part of the current paper by briefly summarizing our overview of the PolyGraphs project and its initial findings. We began by describing the models that we employ in our simulations, building on work by Bala and Goyal ( 1998 ) and others. We then sketched the Zollman effect, whereby there is a tradeoff between accuracy and efficiency in networks of various densities (Zollman, 2007 ). Next, we compared O’Connor and Weatherall’s (2018) polarization models, in which agents mistrust others, using Jeffrey’s (rather than Bayes’) rule to discount the evidence provided by those who are unlike themselves. We found that simulations based on these models resulted in more ignorance overall than did those using Zollman’s original models; and they took longer to overcome that ignorance, even in those cases in which they ultimately did achieve knowledge. Finally, we motivated the idea that we might wish to look at alternative ways of understanding what it is for a group as a whole to believe something, that does not require consensus, and which may be sensitive in some way to the network structure that is present in the group. We indicated that we will pursue a number of these strands further in future work.

Comparing digital humanities and computational social science

We turn now to the comparison of our approach in the PolyGraphs project with other related practices. We begin by sketching a taxonomy of work in this broad area where the human sciences meet digital technology. We then situate PolyGraphs relative to representative projects in the digital humanities and computational social sciences in turn—and in so doing draw out some of its distinctive features as a computational humanities project.

A taxonomy of approaches

What characterizes the digital humanities—‘beyond being an encounter of some sort between the humanities and the digital’ (Luhmann and Burghardt, 2022 , p. 149)? At one extreme, some thinkers are skeptical, finding ‘digital humanities’ to be little more than a buzzword that masks poor quality research (Luhmann and Burghardt, 2022 , p. 149), while ideological critics think the ‘Digital Humanities appeal to university administrators, the state, and high-rolling funders because it [sic.] facilitates the implementation of neoliberal policies’ (Neilson et al., 2018 , p. 4), replacing socially progressive academic work with employment-oriented training. At another extreme (Luhmann and Burghardt, 2022 , p. 149), there are those who hold that, presumably due to a certain methodological superiority, digital humanities will ultimately encompass or replace all work in the humanities.

We come not to evaluate the digital humanities, however, but to understand them—and to use that understanding to situate the approach taken in the PolyGraphs project. To this end, we suggest that a broad division of work in the area of the above ‘encounter’ can be effectuated based on what is being investigated and how . Thus, some research uses computational methods to address questions of traditional interest within the human sciences, while other work uses the techniques of these latter sciences, and takes some aspect of the digital realm as its object of inquiry. We can further distinguish, within the first category above, the digital humanities properly so-called on the one hand, from the computational social sciences (Lazer et al., 2009 ) on the other. The result is a three-way classification of work in this area, which is admittedly rough and ready, with fuzzy boundaries between categories, and some research projects no doubt displaying elements of more than one type of work. Nevertheless, we believe it will prove helpful in what follows.

Roth ( 2019 ) similarly discerns three kinds of work in this broad area of investigation—a fact that lends support to our analysis. Roth writes:

The perhaps most widespread acceptation of ‘digital humanities’ relates to the creation, curation, and use of digitized datasets in human sciences and, to a lesser extent, social sciences. In broad terms, these approaches include the development and application of computer tools to, inter alia, digitize, store, process, gather, connect, manage, make available, mine, and visualize text collections and corpuses, image banks, or multimedia documents of various origins (2019: p. 616).

Roth uses the term ‘digitized humanities’ in connection with work of this kind. Nevertheless, it is this that we will be focusing on when we speak of the digital humanities—work that employs digital methods in service of academic goals that might be recognized by the traditional humanities disciplines. Footnote 9

By contrast, according to Roth, researchers of a second kind ‘develop mathematical frameworks and computer science methods with the specific goal of formalizing and stylizing some systematic social processes’ (2019: 617–618), e.g., by building social simulations, or employing agent-based modeling. Here, she says:

datasets are not anymore exploited as singular recordings corresponding to given empirical case studies, but simply as exemplar instances of a much wider and, more importantly, interchangeable phenomenon. This approach is not dissimilar from the one usually ascribed to natural sciences, in that [researchers] seek… general laws rather than local patterns’ (2019: p. 618).

But Roth notes that ‘in practice, [work of this kind] generally builds more often on social science research issues than humanities’ (2019: 618): thus, whereas she speaks of the ‘numerical humanities’, we follow Lazer et al. ( 2009 ) in referring to this and related work as ‘computational social science’. Footnote 10

Finally, the ‘humanities of the digital’ as Roth calls the third category of work, ‘focuses on computer-mediated interactions and societies, such as the Internet and other online communities’ (2019: p. 623). This may suggest a relatively restricted field, including only, e.g., work on human-computer interaction and/or the philosophy or sociology of technology; though we propose that any work employing the methods of the humanities or social sciences that makes the digital into the object of inquiry is of this character. Work of this third kind is of considerable interest: Roth herself, for instance, concludes by ‘insist[ing] on the possible broker role of the. humanities of the digital bridging the gap between digital humanities and numerical humanities’ (2019: p. 629).

Our proposed threefold taxonomy can accommodate other (e.g., historical) accounts of work in this area. Berry ( 2012 ), for instance, suggests three periods (or ‘waves’) in the development of the digital humanities. In the first wave, traditional objects of humanistic inquiry were digitized, allowing them to be explored using computational techniques. In the second, humanists turned their attention to an expanded range of cultural artefacts, including those that were ‘born-digital’ (2012: p. 4). Berry then suggests ‘a tentative path for a third wave of the digital humanities, concentrated around the underlying computationality of the forms held within a computational medium’ (2012: p. 4) One might expect that this ‘computational turn’ (Berry, 2012: p. 4) would be akin to Roth’s ‘numerical humanities’; but in fact it appears to be closer to her ‘humanities of the digital’—for Berry says that in this endeavor, ‘code and software are to become objects of research for the humanities and social sciences, including philosophy’ (2012: p. 17, our emphasis). In short, the methods of the human sciences are used to investigate digital/computational objects in the third wave (as in, e.g., explorations of algorithmic bias). Footnote 11 Meanwhile, work in Berry’s first two waves is clearly of the ‘digitized humanities’ variety. The computational social sciences are simply ignored.

Given that it is well-suited to the task (e.g., successfully subsuming Berry’s divisions), in what follows we deploy our threefold taxonomy, with its similarities to that of Roth ( 2019 ), in order to compare the approach of the PolyGraphs project with other, related practices. We set aside the humanities and social sciences of the digital as involving a fundamentally different sort of encounter between the digital and the humanities than the other two, and one that is broadly irrelevant to our current purpose of situating the approach taken in the PolyGraphs project. Footnote 12 This leaves us with two comparisons to make, which we undertake in turn: first, with the digital humanities; and then, with the computational social sciences.

Digital humanities and PolyGraphs

For better or for worse, philosophers have not, it seems to us, been ready adopters of the methods employed in the digital humanities. We suspect that there are two central reasons for this. First, philosophers do not typically think of the subject matter of their discipline as consisting primarily of texts (or other human artifacts, such as images). Insofar as texts are investigated in philosophy, this is in order to glean insights into the true subject matter of the field, which is—for want of a better phrase—the human condition; that is to say, at least roughly, various aspects of human experience, the nature of the world we navigate, and how this affects us (both morally and cognitively/epistemically). This leads to the second point. For, insofar as the techniques of the digital humanities are oriented towards the investigation of digital artifacts (e.g., texts) and/or repositories (e.g., journal archives), their investigation may be thought to be at best incidentally related to, and ultimately separable from, philosophical inquiry properly so-called. In short, digital activities may appear to be simply grafted onto a humanistic one. Allow us to give a representative example of where this charge might be levied—whether fairly or not.

Alfano ( 2018 ) aims to ‘explain a synoptic Digital Humanities approach to Nietzsche’s interpretation and demonstrate its explanatory value’ (2018: p. 86). In particular, Alfano is interested in Nietzsche’s views on moral psychology, and specifically how he employs the notions of drive, instinct, and virtue; and he explains, in effect, that after choosing these notions to focus on, he then operationalizes them with words and word stems that are expressive of them, searches a repository of Nietzsche’s texts for occurrences of those textual elements, cleans, analyzes, and visualizes the data he obtains, and then engages in a close reading of relevant passages in Nietzsche’s work that are revealed by that data. As a result of his research, he concludes that, for Nietzsche: (i) instincts and virtues are kinds of drives; (ii) drives are dispositions to perform particular action types; and (iii) drives cannot be easily changed.

It is perhaps worth remarking that in this case, even if there is a broader interest in whether Nietzsche’s moral psychology is ultimately correct (and so in human nature—i.e., an aspect of the human condition), the immediate object of investigation is a body of texts, namely the corpus of Nietzsche’s writings. For this reason, the techniques of the digital humanities are perhaps especially well-suited to the investigation at hand (whereas they might not be appropriate for other philosophical projects). Nevertheless, there is a way of thinking about the project as described in which the specific digital techniques employed are ancillary to the central interpretive work that constitutes the proper humanistic investigation. In effect, there is some ‘humanities computing’ that plays a supporting role in allowing Alfano to identify passages in Nietzsche’s writings to look at; and he then engages in the proper philosophical work of interpreting those passages (through ‘close reading’). From this perspective, the (‘tech support’) role played by the digital element of the project is not unlike that played by a steam-powered train in getting a 19th-century researcher to the library—it may enhance efficiency, but is hardly integral, or essential, to the intellectual work it supports.

This is no doubt an unfair characterization of Alfano’s project, and of the variety of digital humanities work it is here representing. For one thing, part of the argument for the interpretation given concerns the distribution over time of the keywords that express the target notions, and this distribution is discerned through the digital humanities techniques employed. Nevertheless, it is safe to say that the role of the computational methods employed in PolyGraphs is unlike that described in this caricature: they are certainly not dissociable from the intellectual work of the research in which we are engaged. Footnote 13 Our simulations generate evidence that bears directly on philosophical questions. What might happen if a community of agents conducted an inquiry in the manner specified in one of our models? Would knowledge be achieved within the community? Or would ignorance persist? These are questions that interest philosophers—and the computations performed in our simulations are integral to our attempts to address them, not mere addenda to those inquiries.

It is perhaps worth commenting on one further point in connection with the digital humanities, before comparing PolyGraphs to work in the computational social sciences. We have hitherto focused on the use of digital techniques in the early stages of research—roughly, in (or as preparatory to) investigation. But as Neilson et al. ( 2018 ) point out, some think of the digital humanities as disciplines ‘in which students and faculty make things, not just texts’ (2018: p. 3). In this ‘maker turn’ (2018: p. 7), as they call it, ‘publicly available Digital Humanities projects are often part of the demand to retain ownership over one’s work, disseminate information freely, and reach audiences outside of the university.’ (2018: p. 7) Indeed, they note that some in this camp (e.g., futurists) hold that ‘critique now takes place through the design and implementation of new systems’ (2018: 7). In this way, those supporting the maker’s turn might be thought to address the charge of regressive neoliberal appeasement discussed above—on the contrary, it is the digital humanities that are progressive, possibly even revolutionary!

As an example of a project that might be thought to exhibit some of these characteristics, consider Slave Voyages, described on its website as ‘a collaborative digital initiative that compiles and makes publicly accessible records of the largest slave trades in history’. Footnote 14 This is a valuable (and progressive) project, and we ourselves have learned important truths from engaging with it. Nevertheless, it may strike (certain) philosophers that the digital elements here are incidental to the research. In particular, the digital outputs produced—e.g., the two-minute video of Kahn and Bouie ( 2021 ) depicting the voyage of each ship carrying slaves across the Atlantic over a 315-year period—may be thought to primarily facilitate the dissemination of findings, rather than being integral to the research.

Allow us to elaborate on this line of thought. If research is a structured activity aimed at the production of knowledge, then whether that knowledge is disseminated in journal articles or in some other way is not directly relevant to that research. Philosophers in particular may be inclined to hold that propositional, or declarative knowledge (i.e., knowledge that )—rather than either texts or other artifacts —is what research aims to produce. Arguably, such knowledge is most naturally expressed linguistically (rather than, say, graphically, or in terms of images); but there is no inherent reason why it should be expressed in English, for example, rather than French—and so there is no special connection to texts, any more than there is to, e.g., videos. (We might compare Socrates here, who famously never made any of his philosophical contributions in writing.) Philosophers may even be inclined to go so far as to isolate the propositions known as a result of inquiry from the actual knowing of them by specific individuals.

Again, without assessing the merits of this philosophical line of argument, we simply stress that the computational elements in PolyGraphs are not merely supporting dissemination. It is true that we are producing data visualizations as part of the project, and we are releasing the code that performs our simulations on GitHub. The former, we hope, will facilitate the communication of our findings; and the latter constitutes a piece of digital infrastructure that may allow others to conduct further research and obtain new findings. But at its core, PolyGraphs is a computational humanities project (as we will see). How this compares to a project in the computational social sciences is a question to which we now turn.

Computational social sciences and PolyGraphs

PolyGraphs employs models and seeks generalizations, just as certain computational social science projects do. Indeed, the models of information sharing at its heart derive from the social science of economics (Bala and Goyal, 1998 ); and as we have emphasized, even when we apply them to online social networks (as in our analysis above of the EgoFacebook network), our findings should generalize beyond any such particular application to illuminate the phenomena of social epistemology more broadly. Nevertheless, the computational philosophy we practice aims not so much at empirical description and prediction as at answering the kinds of normative and interpretive questions that are distinctive of humanities research.

Computational social science projects typically aim to achieve empirical validation through descriptive accuracy and/or predictive success about some social phenomenon—e.g., the rate at which fake news articles spread on social media. However, they often involve highly simplified ‘agents’—for instance, ones whose actions are restricted to either sharing/re-tweeting a story or not (Menczer and Hills, 2020 ). Plausible causal mechanisms—such as attentional overload (Weng et al., 2012 )—may be identified; however, the nodes of the networks in these studies cannot be readily regarded as occupied by human subjects, with beliefs and desires of their own, who may behave rationally or not. Footnote 15

By contrast, PolyGraphs is concerned with precisely such issues. Can individual agents plausibly be interpreted as having credences that they update using Bayes’ rule? Ought they to use Jeffrey’s rule instead? PolyGraphs addresses these (and other) interpretive and normative questions. For instance: are we able to understand collective action in terms of group attitudes—including beliefs? If so, how ought groups to aggregate their attitudes from those of their members? Such questions are paradigmatically humanistic—and we use computational techniques (specifically, simulations) to investigate them. In other words, PolyGraphs is a humanities project with a computational methodology.

In comparing PolyGraphs to research in the computational social sciences, we have stressed both the character of the questions involved and the corollary that validation is not straightforwardly empirical. Footnote 16 There has been some recent discussion of modeling in philosophy which may illuminate these points. Thus, Williamson ( 2017 ), for example, notes that in the natural and social sciences models are often tested by way of measurable quantities and that this is not possible for (at least some) models in philosophy. However, he stresses that scientific models are also sometimes tested through qualitative predictions—and that philosophical models can and do yield such predictions. Crucially (from our point of view), when it comes to qualitative distinctions of category, some judgment may be required to apply them—and thereby gain the ‘model-independent knowledge of the target phenomenon’ that, as Williamson notes, is required for the testing of those models. In our case, for instance, the prediction of a given model (using Bayes’ or Jeffrey’s rule) might be that a community of rational agents in certain specific circumstances that aggregates its members’ attitudes in some particular way will be ignorant (rather than knowledgeable) of the fact that treatment B is better than treatment A after exposure to this or that course of evidence. If we can independently ascertain whether that would indeed be the case, we can use this knowledge to test our model’s assumptions surrounding the nature of (individual and group) rationality (e.g., whether the update and aggregation rules it employs are the ones that ought to be used in a community of that kind in those circumstances). Footnote 17 But of course, the categorical difference between knowledge and ignorance is quite high-level, and not ‘observational’: an exercise of judgment is required in order to determine how to apply it in a given case. Footnote 18

We have emphasized not only that our investigation employs modeling, but also that it addresses normative questions. In recent work, Titelbaum ( manuscript ) discusses normative modeling. He suggests that normative models are distinguished from descriptive models by the character of the facts they aim to capture—namely, normative, rather than descriptive, facts. Footnote 19 We note, however, that such normative facts cannot be simply ‘observed’. Yet perhaps this point is more readily made in connection with the account of normative modeling given by Colyvan ( 2013 ). ‘Normative models, Colyvan notes, ‘are not supposed to model actual behavior or explain actual behavior; rather, they are supposed to model how agents ought to act.’ (2013: p. 1338, emphasis original) Since, unlike actual behavior, how agents ought to act (including what opinions they ought to form) cannot be directly detected by empirical methods, normative models (including, arguably, those we employ) cannot be validated (or refuted) through overly simplistic (‘positivistic’) appeals to empirical evidence. The judicious exercise of judgment is required.

In comparing PolyGraphs with other projects in the computational social sciences, we have attempted to show that, while there are similarities in approach, subtle differences remain. Our computer simulations rely on (what are intended to be) generally applicable models, but the models involved are arguably normative in character, and accordingly cannot be tested in a flat-footedly empirical manner. We have argued that this befits the humanistic nature of our inquiry.

We began with an overview of the PolyGraphs project, covering prior results (the Zollman effect), and comparing polarization models (due to O’Connor and Weatherall), before briefly considering (our innovative, structure-sensitive approach to) group belief. We then gave a three-way distinction amongst aspects of the ‘encounter’ between the digital (on the one hand) and the humanities and social sciences (on the other). In particular, we distinguished digital humanities, computational social science, and the investigation of the computational and digital using the methods of the human sciences. We argued that whereas some digital humanities projects (appear to) merely append some computational elements either before or after a thoroughly humanistic investigation, in PolyGraphs the computational elements are integral to the research itself. But in contrast to certain computational social science projects, the research questions in PolyGraphs are both normative and interpretive in character. In short, PolyGraphs is a computational humanities project.

Data availability

The datasets generated and analyzed during the current study are available in the GitHub repository , as is our source code.

P i is the initial probability function (prior to update), P f the final probability function (afterwards).

This is the stopping condition we have employed in our simulations, following O’Connor and Weatherall ( 2018 ). Zollman himself originally required B believers to have credence above 0.9999 (2007: 579); and in Zollman ( 2010 ) he allowed simulations to stop after 10, 000 steps. The simplification in the text does not affect our discussion.

He investigated various further network structures as well.

Subsequent work by Rosenstock et al. ( 2017 ) found that these results held only for relatively small networks, with small numbers of (patients, or more generally) trials, and small values of ϵ . Nevertheless, in such cases, Zollman’s two findings were confirmed—and of course, many social epistemological phenomena are approximated by the (small) parameter values in question (e.g., those involving families, committees, or scientific communities with limited evidence-gathering resources).

This means that there is no ‘anti-updating’—receiving the uncertain evidence that e never makes an agent give e less credence than they previously did. Discounting without anti-updating might be an appropriate attitude to take towards ‘bullshitters’—cf. Frankfurt ( 2005 ). With known liars supplying one’s evidence, by contrast, anti-updating might be appropriate. While O’Connor and Weatherall explore an implementation of Jeffrey’s rule with anti-updating, we do not consider it here.

Knowledge requires justified true belief. The consensus belief that B is better is true when it arises in our simulations—and the agents involved update their beliefs in a rational manner, based on the evidence available to them, so their beliefs are justified. Thus, we here treat the consensus that B is better as group knowledge, and its absence—whether through error (false consensus) or omission (e.g., through polarization)—as group ignorance.

more careful discussion would distinguish (i) what a group believes from what its members (ii) severally and (iii) collectively believe (Ball, 2021 ). We do not believe the neglect of this distinction in the main text affects our central points here.

In future work we intend to run our simulations on much larger, real-world networks; but the EgoFacebook graph discussed in the main text already suffices to make our main point here.

This chimes with Luhman and Burghardt’s finding—based on a computational analysis of research articles across a range of journals—that ‘textual data. continue [sic.] to be the predominant object of study in DH’ (2022: p. 167).

Luhman and Burghardt identify Roth’s numerical humanities with what they call ‘computational humanities’—which, they say, ‘approaches humanities research questions through computational models’ (2022: 149). This would be an apt description of the PolyGraphs project—but when we look, for instance, at the website for the research group Burghardt leads, we are told that humanities computing asks, inter alia, ‘How can humanities data—which is traditionally interpreted in an idiographic, hermeneutic way—be modeled in a way it becomes available for computational, empiric analyses?’ (See https://www.mathcs.uni-leipzig.de/en/ifi/research/computational-humanities . Accessed: 14/02/23.) As we will see below, the computational approach to philosophy practiced on the PolyGraphs project preserves a role for interpretation (and, indeed, normativity); and the quote above in any case seems to suggest only a more computationally sophisticated/intensive version of Roth’s digitized humanities.

Note that there may also be a hint here of a connection to the maker turn discussed below. As we indicated, our proposed taxonomy is rough and provisional, with some projects lying between, or even spanning boundaries.

That said, the present work may belong to this third category (even if PolyGraphs itself does not).

We do not wish to suggest that it is only in PolyGraphs that computational work is integral to philosophical investigations. Many others have done work with this character—for just a few examples, in addition to the work by O’Connor, Weatherall, Zollman, and others discussed above, see e.g., Hegselmann and Krause ( 2002 ); Mayo-Wilson ( 2014 ); Olsson ( 2013 ); Pollock ( 1989 ); Skyrms ( 2010 ). For an overview of work in this area, see Grim and Singer (2022).

See https://www.slavevoyages.org/ . Accessed: 14/02/23.

In a similar spirit, Lazer and co-authors note that, amongst thousands of recent papers drawing on the platform’s data, ‘the large majority of Twitter research is making inferences about accounts or tweets; very little of Twitter research can reasonably claim to be making statements about the behaviors of humans’ (Lazer et al., 2021 , p. 191). But even ‘very detailed agent-based approaches’ (Balcan et al., 2009 , p. 21848) in the computational social sciences, which do tell us about the behavior of human beings, often fail to illuminate personal level motivations that would allow us to regard those behaviors as actions. Instead, we get, e.g., ‘realistic estimates of population mobility’ (Eubank et al., 2004 , p. 180). This may be appropriate given the research aims—in this case understanding ‘the relative merits of several proposed mitigation strategies for smallpox spread’ (Eubank et al., 2004 , p. 180). Our point here is simply to contrast the impersonality of such research with that undertaken in PolyGraphs.

In her influential discussion of the humanities, Small ( 2013 ) likewise notes that they ‘focus... on interpretation and critical evaluation’ (2013: 23) and involve ‘an ineliminable element of subjectivity’ (2013: p. 23). Specifically, on this last point, she claims that there is a need in humanities research for an exercise of judgment rather than ‘positivistic appeals to evidence’ (2013: p. 23). We take this to vindicate our claims in the main text—particularly once it is recognized that ‘critical evaluation’ is what ultimately underpins normative assessment.

That ignorance is (epistemically) worse than knowledge is an evaluative claim—and therefore relevant to (strictly) normative questions about how agents ought to behave in relation to opinion formation.

Indeed, judgment can sometimes be required even to determine whether supposedly ‘observational’ categories apply: think of the task of determining whether some color sample that borders on being orange counts as red.

Titelbaum explains that the normative facts, in his view, may be general or particular, and include both prescriptions and evaluations. Others—e.g., Dietrich and List ( 2017 )—regard the normative as strictly distinct from, though related to, the evaluative. We incline slightly towards this latter view, but we do not think anything of significance turns on the issue here.

Alfano M (2018) Digital humanities for history of philosophy: a case study on Nietzsche. In: Neilson T, Levenberg L, Rheams D (eds.) Research methods for the digital humanities. Springer, Cham, pp. 85–101

Bala V, Goyal S (1998) Learning from neighbours. Rev Econ Stud 65:595–621

Article   Google Scholar  

Balcan D (2009) Multiscale mobility networks and the spatial spreading of infectious diseases. Proc Natl Acad Sci USA 106:21484–21489

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Ball B (2021) Groups, attitudes and speech. Analysis 81:817–826

Ball B, Koliousis A (2022) Training philosopher engineers for better AI. AI Soc 38:1–8

Google Scholar  

Berry DM (2012) Introduction: understanding the digital humanities. Palgrave Macmillan UK, London. pp. 1–20

Cassam Q (2018) Vices of the mind: from the intellectual to the political. Oxford University Press

Colyvan M (2013) Idealisations in normative models. Synthese 190:1337–1350

Article   MathSciNet   Google Scholar  

Dietrich F, List C (2017) What matters and how it matters: a choice-theoretic representation of moral theories. Philos Rev 126:421–479

Eubank S (2004) Modelling disease outbreaks in realistic urban social networks. Nature 429:180–184

Article   ADS   CAS   PubMed   Google Scholar  

Frankfurt H (2005) On bullshit. Princeton University Press

Goldman AI.(1999) Knowledge in a social world. Oxford University Press, Oxford, England

Grim P, Singer D (2022). Computational philosophy. In: Zalta EN, Nodelman U (eds.). The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Fall 2022 edition

Hegselmann R, Krause U (2002) Opinion dynamics and bounded confidence models, analysis and simulation. J Artif Soc Soc Simul 5. https://jasss.soc.surrey.ac.uk/5/3/2.html

Kahn A, Bouie J (2021) The Atlantic slave trade in two minutes. Available at https://slate.com/news-and-politics/2021/09/atlantic-slave-trade-history-animated-interactive.html . Accessed: 14/02/23

Kahneman D (2011) Thinking, fast and slow. Macmillan

Lazer D (2021) Meaningful measures of human society in the twenty-first century. Nature 595:189–196

Lazer D (2009) Computational social science. Science 323:721–723

Article   CAS   PubMed   PubMed Central   Google Scholar  

Leskovec J, Mcauley J (2012) Learning to discover social circles in ego networks. Adv Neural Inf Process Syst 25. https://papers.nips.cc/paper_files/paper/2012

Luhmann J, Burghardt M (2022) Digital humanities–a discipline in its own right? an analysis of the role and position of digital humanities in the academic landscape. J Assoc Inf Sci Technol 73:148–171

Mayo-Wilson C (2014) Reliability of testimonial norms in scientific communities. Synthese 191:55–78

Mayo-Wilson C, Zollman K (2021) The computational philosophy: simulation as a core philosophical method. Synthese 199:3647–3673

Article   MathSciNet   PubMed   PubMed Central   Google Scholar  

McCarty W (2003) Humanities computing. Encyclopedia Libr Inf Sci 2:1224

Menczer F, Hills T (2020) The attention economy. Sci Am 323:54–61

Neilson T, Levenberg L, Rheams D (2018) Introduction: research methods for the digital humanities. Res Method Digit Humanit 1–14

O’Connor C, Weatherall JO (2018) Scientific polarization. Eur J Philos Sci 8:855–875

Olsson E (2013) A Bayesian simulation model of group deliberation and polarization. In: Zenker F (ed.) Bayesian argumentation, Springer

Pollock J (1989).How to build a person: a prolegomenon. MIT Press. Cambridge, MA: MIT Press

Rosenstock S, Bruner J, O’Connor C (2017) In epistemic networks, is less really more? Philos Sci 84:234–252

Roth C (2019) Digital, digitized, and numerical humanities. Digit Scholarsh Humanit 34:616–632

Skyrms B (2010) Signals: evolution, learning, and information. Oxford University Press, Oxford, England

Small H (2013) The value of the humanities. Oxford University Press

Titelbaum MG (manuscript) Normative modeling

Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Sci Rep 2:335

Williamson T (2017) Model-building in philosophy. Philosophy’s future: the problem of philosophical progress. John Wiley & Sons, Inc. pp. 159–171

Zollman KJ (2010) The epistemic benefit of transient diversity. Erkenntnis 72:17–35

Zollman KJS (2007) The communication structure of epistemic communities. Philos Sci 74:574–587

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Acknowledgements

This work was supported by the British Academy, the Royal Academy of Engineering, the Royal Society, and the Leverhulme Trust, under the APEX Award scheme, grant number APX\R1\211230, and by Northeastern University’s NULab for Text, Maps, and Networks. The authors also gratefully acknowledge support for this publication from the Digital Academy at the Academy of Sciences and Literature Mainz, Germany.

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Ball, B., Koliousis, A., Mohanan, A. et al. Computational philosophy: reflections on the PolyGraphs project. Humanit Soc Sci Commun 11 , 186 (2024). https://doi.org/10.1057/s41599-024-02619-z

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Members of the Faculty are currently involved in these research projects:

Angela Breitenbach has a Leverhulme Research Fellowship to work on Beauty in science: a Kantian approach to aesthetics in the study of nature

Alex Oliver is one of the leaders, with Prof Boudewijn de Bruin of the University of Groningen, of the NWO funded project, ‘ Trusting Banks ’. Richard Holton is also a member of this project.

Rae Langton and Richard Holton are participants in the ‘ Implicit Bias and Philosophy ’ Research Project based at Sheffield.

Huw Price is a joint leader (with Dr Chris Timpson and Dr Owen Maroney, Oxford ) of the project, ‘Information at the Quantum Physics/Statistical Mechanics Nexus: Entropy, Time Asymmetry, Probability and Perspective’ funded by the Templeton World Charity Foundation . 

Huw is also one of the leaders of the project ‘ New Agendas for the Study of Time ’,   based in Sydney and funded by the John Templeton Foundation . 

John Marenbon is one of the leaders of the project ' Immateriality, Thinking and the Self in the Philosophy of the Long Middle Ages ', a joint project of the Faculty of Philosophy, University of Cambridge and the Department of Philosophy, Peking University.

Members of the Faculty are also involved in these research networks:

Angela Breitenbach is part of the Leverhulme International Network on Kant and the Laws of Nature: Lessons from physical and life sciences of the 18th century , based at the University of Edinburgh.

Angela is also a member of the Luxembourg National Research Foundation funded network on Contemporary Kantian Philosophy , based at the University of Luxembourg.

Michael Potter is a founding member of the Cambridge – Notre Dame – Munich Philosophy of Mathematics Triangle, and a member of the Early Analytic Philosophy Group in Scotland, based at the University of Stirling. 

Some past research projects and networks

• New Directions in the Study of the Mind
• Immateriality, Thinking and the Self in the Philosophy of the Long Middle Ages

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Research Overview

Research Areas

Stanford Philosophy is a dynamic environment supporting ongoing research by faculty and graduate students in almost all areas of the field.

Contemporary Theoretical Philosophy including Metaphysics , Epistemology , Philosophy of Language , Philosophy of Mind

Ethics , Applied Ethics , and Political Philosophy

History of Philosophy ( Ancient Philosophy , K ant , Early Modern Philosophy ,  20th Century Philosophy )

Logic and Formal Philosophy

Philosophy of Action

Philosophy of Science

You can learn about groups of faculty working in a particular area by clicking on that area of interest next to any faculty member’s profile on the  main faculty page .

More detailed information about faculty research interests and how they fit together is included in each faculty member’s profile.

The Department also hosts many active  reading groups  and  workshops .  

Learn more about reading groups and workshops

Department of Philosophy

Current & recent funded research projects, current projects, recent projects.

• Department of Philosophy

Research centres and projects

The department is home to the Hang Seng Centre for Cognitive Studies, the Centre for Engaged Philosophy, the Centre for the History of Philosophy, Philosophy in the City, and a number of major research projects.

Research centres

Hang seng centre for cognitive studies.

The Hang Seng Centre for Cognitive Studies is based in the Department and directed by Stephen Laurence ,  Luca Barlassina , and Gerardo Viera . It organizes seminars, workshops, and conferences to address foundational issues in the study of the mind and cognition. Since its founding in 1992, there have been more than 40 workshops and conferences, resulting in seven volumes, published by Cambridge and Oxford University Presses.

Centre for the History of Philosophy

The Centre for the History of Philosophy (ChiPhi) is the UK's largest group of scholars working in the history of philosophy, composed of philosophers from Leeds, Sheffield, and York. It brings together staff and postgraduates from the three Universities to consolidate the wide-ranging expertise into a virtual centre for the History of Philosophy that exceeds the resources and expertise of any one UK University. Please contact  Komarine Romdenh-Romluc  for more information.

Centre for Engaged Philosophy  

The Centre for Engaged Philosophy, co-directed by Jules Holroyd and Joshua Forstenzer , promotes philosophy that engages deeply in and with real-world issues of significance such as race and racism and ethics in health care. It brings together scholars and practitioners dedicated to philosophical practices that aim to inform, learn from, and build, ongoing collaborative relationships of import beyond the academy. 

Current research projects

Philosophy in the City

Philosophy in the City is a volunteering award-winning outreach project, run entirely by student volunteers from the University of Sheffield’s Philosophy department. It aims to improve the opportunities available to those within the local community to engage with Philosophy; the opportunities for people of any age or background to engage with Philosophy, and to make Philosophy a subject that is of use and value to both the individual and society. 

PinC volunteers go into schools and other institutions to teach philosophy and to encourage pupils and residents to think critically about philosophical problems and develop their own ideas. 

Getting back in touch: Emotional pathways to a post-pandemic world  -  Luca Barlassina

Luca Barlassina is the PI for this WUN-funded interdisciplinary Research Project that studies emotional responses to the Covid-19 pandemic across four continents (Africa, Asia, Europe, and South America), with the aim of designing public health strategies that can maximise emotional well-being and foster post-traumatic growth. 

Philosophy in Prison - Jim Chamberlain

The project, in collaboration with Philosophy in Prison , co-developed, designed and delivered a prototype course for testing a 4-week prison Philosophy course. There are clear benefits to doing philosophy in prisons. The UK Government’s recent Prisons Strategy White Paper stresses the importance of providing prisoners with education, to improve their chances of employment on leaving prison, and structured activities, to reduce the risks of violence and bullying that come with excessive unstructured time. The module delivered by this project will employ conversation as a methodology for doing philosophy. In this way, it will make philosophy accessible to a wide range of prisoners, including those with few or no educational achievements, learning disabilities, and with English as a second or third language. Moreover, the Department has long-term plans to develop an accredited course in Philosophy for delivery in prison and this project will, among other things, be a proof of concept of a teaching methodology which will support these longer-term plans.

Climate Crisis Education -  Joshua Forstenzer

How should formal and informal education be reformed in light of the climate crisis? This is the question at the heart of the Knowledge Exchange project led by Joshua Forstenzer and funded by UKRI's Higher Education Innovation Fund. This project saw the establishment of an international partnership between  Union of Justice ,  Synergie Family , and the University of Sheffield's Philosophy Department. This partnership serves to bring together practitioners in the field of education, climate action activists, decision-makers, and philosophers to foster a sustained conversation about political and civic education, science education, arts education, physical education, sports coaching, work training, leadership training in response to the ways in which the rapid warming of climate and associated dangers require curricular, pedagogic, and policy changes at various levels of education and in diverse educational contexts. Commitments to democratic principles of education and the need to build in experiences of meaningful value that contribute to personal and collective flourishing are taken as starting points in these discussions, even though their continued meaning in a potentially hostile political and natural environment is brought into question.

Knowledge of Mere Possibilities - Dominic Gregory

Some facts are necessary, while others could have been different. Yet our most basic sources of evidence are apparently firmly confined to actuality: my eyes show that it is sunny outside but not whether it could instead have been raining. How therefore do we know that things could have been other than they actually are? Despite the importance of this knowledge to philosophy and everyday life, many basic questions about it remain relatively neglected. This project, funded by the Leverhulme Trust, will develop detailed answers to the philosophical questions raised by knowledge of mere possibilities, while also assessing the resulting view's leading rivals.

MIND Fellowship - Jules Holroyd

Philosophers have written a lot about blame, but much less about praise. One might think that while blaming might be a morally problematic activity (for example, if it is excessively harsh, or hypocritical, or where victim-blaming occurs), praising is always morally neutral, if not morally positive. I argue that this is a mistake: praise can in fact be implicated in oppression: perpetuating stereotypes and distorting the contours of moral agency. Articulating what is going wrong, the implications for theories of moral responsibility, and, ultimately, how to praise better, requires careful philosophical attention. My project is to develop a book that articulates an account of what praise should do, if it is to be justified, and its role in our practices of holding responsible. This builds on my recent work on the oppressive aspects of praise, published in  Feminist Philosophy Quarterly * (open access). 

* Holroyd, Jules. 2021. “Oppressive Praise”.  Feminist Philosophy Quarterly  7 (4).  https://ojs.lib.uwo.ca/index.php/fpq/article/view/13967 .  

PhilonoUS, the Sheffield Undergraduate Philosophy Journal is run by current undergraduate Philosophy students and showcases some of the best undergraduate work from Sheffield, as well as other national and international undergraduate students.

Other recent projects

Ideas gallery

Daniel Herbert and Daniel Viehoff collaborated with the  National Civil War Centre  in Newark on an AHRC funded cultural engagement project to create an 'Ideas Gallery'. The Ideas Gallery aimed to introduce some of the philosophical and political ideas- such as political and social equality, religious toleration, freedom of expression, and authority based on a social contract- that played an important part in the Civil War era and still influence our thinking today. The political philosophies of Thomas Hobbes and John Locke, and their subsequent legacies, were particular points of focus for this project.

Modelling International Cooperation Between States

Holly Lawford-Smith's Marie Curie FP7 Grant "Modelling International Cooperation Between States" is a three-year project aiming to both model cooperation between state agents, and make recommendations about the conditions under which such cooperation is likely to be successful, with a final view to commenting on current negotiations over climate change. The first stage of the project focuses on the nature of states as collective agents. The second stage focuses on whether state agents behave sufficiently similarly to ordinary human agents in at least some contexts that certain lessons from the wide experimental literature on cooperation between human agents apply across. You can watch Holly give a talk about this project  here  (overview from 1.02-15.40).

Democracy and Criminal Justice

The Democracy and Criminal Justice project inquires into the justice of denying prisoners the right to vote.  It asks whether offenders should lose any of the rights of citizenship and if so, to what extent they should helped to regain them.

Journal of Applied Philosophy

Chris Bennett has been a former editor of The Journal of Applied Philosophy and he sits on the Editorial Board. The journal specializes in promoting philosophical research having direct bearing on areas of practical concern.

White Rose Aesthetics Forum

The White Rose Aesthetics Forum involves philosophers from the Universities of Hull, Leeds, Sheffield, and York. 

Bias and Blame   Jules Holroyd

Jules Holroyd 's Leverhulme Trust Project Grant investigated the impact of moral interactions on the expression of implicit bias. It was an interdisciplinary project with  Dr Tom Stafford  and Dr Robin Scaife who are based in the Department of Psychology at The University of Sheffield. The aim of the project was to gain a better understanding of how interpersonal interactions can be harnessed to combat discrimination due to implicit racial bias. We have also turned our attention to the sorts of institutional changes that may serve this task, and the sorts of interpersonal interactions that motivate institutional change.

AHRC Culture and the Mind

The AHRC Culture and the Mind project is a major five-year interdisciplinary research project based in the Philosophy Department at the University of Sheffield. The project is funded primarily through a major research grant of £538,000 from the UK Arts and Humanities Research Council (to the project director, Stephen Laurence). The project brought together top scholars in a broad range of disciplines to investigate the interaction of culture and the mind and it’s philosophical consequences. 

Idealism & Pragmatism: Convergence or Contestation? 

This research project, funded by the Leverhulme Trust, aimed to explore the connections between these seemingly opposed philosophical traditions.  It linked the Sheffield Philosophy Department with those at Cambridge, Columbia, Frankfurt, Pittsburgh, Sydney, Vanderbilt, and the Collège de France.

Implicit Bias and Philosophy

The Implicit Bias and Philosophy project brought together an international team of philosophers, psychologists, and policy professionals to reflect upon the phenomenon of implicit bias; the project was funded by the Leverhulme Trust and the University of Sheffield.

The University's cross-faculty research centres harness our interdisciplinary expertise to solve the world's most pressing challenges.

Research Philosophy

Research philosophy is a vast topic and here we will not be discussing this topic in great details. Research philosophy is associated with assumption, knowledge and nature of the study. It deals with the specific way of developing knowledge. This matter needs to be addressed because researchers may have different assumptions about the nature of truth and knowledge and philosophy helps us to understand their assumptions.

In business and economics dissertations at Bachelor’s level, you are not expected to discuss research philosophy in a great level of depth, and about one page in methodology chapter devoted to research philosophy usually suffices. For a business dissertation at Master’s level, on the other hand, you may need to provide more discussion of the philosophy of your study. But even there, about two pages of discussions are usually accepted as sufficient by supervisors.

Discussion of research philosophy in your dissertation should include the following:

• You need to specify the research philosophy of your study. Your research philosophy can be pragmatism , positivism , realism or interpretivism as discussed below in more details.
• The reasons behind philosophical classifications of the study need to be provided.
• You need to discuss the implications of your research philosophy on the research strategy in general and the choice of primary data collection methods in particular.

The Essence of Research Philosophy

Research philosophy deals with the source, nature and development of knowledge [1] . In simple terms, research philosophy is belief about the ways in which data about a phenomenon should be collected, analysed and used.

Although the idea of knowledge creation may appear to be profound, you are engaged in knowledge creation as part of completing your dissertation. You will collect secondary and primary data and engage in data analysis to answer the research question and this answer marks the creation of new knowledge.

In respect to business and economics philosophy has the following important three functions [2] :

• Demystifying : Exposing, criticising and explaining the unsustainable assumptions, inconsistencies and confusions these may contain.
• Informing : Helping researchers to understand where they stand in the wider field of knowledge-producing activities, and helping to make them aware of potentialities they might explore.
• Method-facilitating : Dissecting and better understanding the methods which economists or, more generally, scientists do, or could, use, and thereby to refine the methods on offer and/or to clarify their conditions of usage.

In essence, addressing research philosophy in your dissertation involves being aware and formulating your beliefs and assumptions.  As illustrated in figure below, the identification of research philosophy is positioned at the outer layer of the ‘research onion’. Accordingly it is the first topic to be clarified in research methodology chapter of your dissertation.

Research philosophy in the ‘research onion’ [2]

Each stage of the research process is based on assumptions about the sources and the nature of knowledge. Research philosophy will reflect the author’s important assumptions and these assumptions serve as base for the research strategy. Generally, research philosophy has many branches related to a wide range of disciplines. Within the scope of business studies in particular there are four main research philosophies:

• Interpretivism (Interpretivist)

The Choice of Research Philosophy

The choice of a specific research philosophy is impacted by practical implications. There are important philosophical differences between studies that focus on facts and numbers such as an analysis of the impact of foreign direct investment on the level of GDP growth and qualitative studies such as an analysis of leadership style on employee motivation in organizations.

The choice between positivist and interpretivist research philosophies or between quantitative and qualitative research methods has traditionally represented a major point of debate. However, the latest developments in the practice of conducting studies have increased the popularity of pragmatism and realism philosophies as well.

Moreover, as it is illustrated in table below, there are popular data collection methods associated with each research philosophy.

 Research philosophies and data collection methods [3]

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research philosophy. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis  are explained in this e-book in simple words.

John Dudovskiy

[1] Bajpai, N. (2011) “Business Research Methods” Pearson Education India

[2] Tsung, E.W.K. (2016) “The Philosophy of Management Research” Routledge

[3] Table adapted from Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

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Research Centers & Projects

The minnesota center for philosophy of science.

The Minnesota Center for Philosophy of Science (MCPS) advances research and graduate training in the philosophy of science and related studies of science and technology. It fosters a local community through a variety of activities and special events. This local community includes scholars from a number of different disciplines throughout the University of Minnesota as well as area colleges and universities. The center brings together researchers from around the world through its visiting fellow program and conferences, and conducts collaborative research through its workshops, the results of which are published in Minnesota Studies in the Philosophy of Science . Current volumes in this series can be purchased through the  University of Minnesota Press .

The mission of MCPS is to promote excellence in research and training in philosophy of science and related empirical studies of science. It was founded in 1953 by Herbert Feigl, a member of the famous “Vienna Circle” and the first to immigrate to North America. Feigl was well known for his “intellectual hospitality” and MCPS quickly became the gathering place for leading proponents of logical empiricism. The establishment of MCPS went hand in hand with the establishment of logical empiricism as the dominant philosophy of scientific knowledge. MCPS is no longer committed to a single perspective and now seeks to promote a plurality of views about science and from a multiplicity of perspectives including historical and social scientific as well as philosophical. MCPS is both an international center of research and a local center that brings together scholars from multiple departments, colleges, and universities in our geographical region. It sponsors a variety of activities including multiple discussion groups, colloquia, and special events such as the annual science studies colloquium.

MCPS provides the administrative support for the Graduate Minor in Studies of Science and Technology (SST) and also the Conceptual Foundations of Evolutionary Biology Graduate Group. It works collaboratively with a number of groups at the University including the History of Science, Technology, and Medicine Graduate Program (HSTM), the Department of Philosophy , and the Theorizing Early Modern Studies Research Collaborative (TEMS).

Writing-Enhanced Curriculum (WEC) Project

The Department of Philosophy is enrolled in the University of Minnesota's WEC project, which provides a process for meaningfully infusing writing and writing instruction into the undergraduate curricula. An annual workshop series (jointly facilitated by graduate students and faculty) helps provide support for graduate student instructors and TAs working to infuse writing instruction into their teaching.

For more information about the department's work with the project, please contact Roy Cook at [email protected].

Research Projects

Early modern mobility: knowledge, communication and transportation, 1500-1800.

A two year project of teaching, research, and publications supported by a UPS grant and the HPS program, STS, Urban Studies, Communications and Spatial History Lab.

Mapping the Republic of Letters

Mapping the Republic of Letters  is a collaborative, interdisciplinary, and international project in the digital humanities involving faculty, staff, and students. Since 2008, the scope of the project has been creating visualizations to analyze "big data" relating to the world of early-modern scholars focusing primarily on their correspondence, travel, and social networks. While quantitative metrics to examine the scope and dimensions of the data are utilized, the project remains committed to the qualitative methodologies of the humanities.

Gendered Innovations in Science, Health & Medicine, Engineering, and Environment

Professor Londa Schiebinger's internationally funded and peer-reviewed project to employ sex and gender analysis within the fields of Science, Health & Medicine, Engineering and the Environment. Gendered Innovations as a resource to create new knowledge and technology.

The  Gendered Innovations  project:

• develops practical methods of sex and gender analysis for scientists and engineers;
• provides case studies as concrete illustrations of how sex and gender analysis leads to innovation.

Visiting Scholars

In 2022-23 we are hosting five visiting scholars:

• Professor Susanne Schmidt, Humboldt University Berlin postdoc fellowship;
• Professor Nataly Valdez, Barcelona Supercomuting Center Scholar;
• Chandra Vadhana, PhD, Fullbright Awardee and Visiting Postdoc Fellow;
• Jens Amborg, PhD candidate, Humboldt Student Researcher from Uppsala Sweden; and
• Huimin Huang, PhD candidate, China

We’re driven by unrelenting curiosities

As an organization, we maintain a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work.

A flexible research environment

We strive to create an environment conducive to many different types of research across many different time scales and levels of risk..

In recent years, computing has both expanded as a field and grown in its importance to society. Similarly, the research conducted at Google has broadened dramatically, becoming more important than ever to our mission . As such, our research philosophy has become more expansive than the hybrid approach to research we described in our CACM article six years ago and now incorporates a substantial amount of open-ended, long-term research driven more by scientific curiosity than current product needs.

We believe successful industry research requires managing a portfolio of projects with time horizons, levels of risk and goals appropriate for the organization. Our approach to research has always been flexible, but there are two reasons that our research philosophy today adds more fundamental, or "pure basic," research than it has previously.

First, Google's increasingly diverse businesses, longer-term outlook and greater scale let us pursue ambitious projects that involve more technical risk than ever before.

Our hybrid research model was explicitly designed to achieve success in this kind of environment, but we have learned we need to extend it further. Second, machine learning (ML) is a transformative technology that touches everything we do as a company. Therefore, fundamental advances in machine learning technology are likely to produce value across the organization, even when developed without a close connection to a specific application or product.

ML is not unique in this regard; the same argument applies to other technologies outside of ML that are critical to the company.

Our research spans four main dimensions

Our goal is to create a research environment rich in opportunities for product impact, to build a product environment that actively benefits from research, and to provide our staff the freedom to work on important research problems that are not tied to immediate product needs. Some of the most exciting research enables new products, or even new businesses, that we cannot imagine today.

Given the diversity of research projects that we pursue, we have found it useful to define four types of work to help crystalize the goals of projects and allow us to measure progress. Although the organization as a whole needs to include all four types of work, teams or individuals typically emphasize a subset of them.

Such projects address key scientific or engineering questions or develop fundamentally new capabilities. Successful outcomes might be better technology, useful theories or new discoveries. We use established scientific benchmarks or develop new ones for measuring progress. Although these projects can tackle scientific problems motivated by user/product needs, they often move faster when conducted independently of existing products.

These projects explore and develop new products or even new businesses that require substantial research. The necessary research typically has either significant depth and enables new capabilities or significant breadth and combines a variety of technologies in novel ways. Near-term measures of success include demos, prototypes or pilots that prove user / customer utility and analyses that meaningfully inform research or business priorities. Long-term, these efforts will ultimately be measured by business or human impact (e.g. profit, usage scale, strategic positioning).

Most of our products stretch the boundaries of what is technically possible and benefit from active development of what would be characterized as research in other environments. Individuals operating in this domain may call themselves researchers or engineers and may or may not report into the product team, but in all cases their work is characterized by deep, essential contributions to products. Progress is measured against product metrics, although research publications are often a side effect. These efforts often motivate additional research questions that can be studied either directly in the context of the product or abstracted into standalone fundamental research projects.

Such projects create reusable components that enhance the work of (product and research) teams that adopt them, multiplying everyone’s impact. The success of these projects is measured by adoption. Sometimes infrastructure projects arise out of repeated product contributions that crystalize how such contributions can be better addressed through a common solution. In other cases the infrastructure co-evolves with its initial uses or even anticipates future needs.

Machine Translation started as a fundamental research exploration and became a product when the translation quality reached a sufficiently high quality bar.

Spanner is a global scale-out database based on new research motivated by product needs and delivered as an internal- and external-facing product.

Robotics is a new research effort to understand how machine learning can help robots become more useful in the world.

This work is not mutually exclusive

There can be projects that pursue multiple goals at once: for example, there are cases where scientific metrics correlate well with product metrics and optimizing for both fundamental research and new product innovation or critical product contributions is possible: machine translation research and the Google Translate product are examples of this combination.

Similarly, the goals of projects can change during their lifetime: many projects that start as fundamental research projects produce artifacts that can shift the objectives of the entire project or can lead to new projects that apply the technology. Importantly, we try to not force such transitions or (even worse) specific timelines, but we make sure to measure progress and milestones for all projects, using metrics appropriate for the project and the dimension(s) it currently focuses on.

We work closely with the broader scientific community

A final essential component of our philosophy is supporting the free exchange of ideas and maintaining close contact with the broader scientific community, in academia and industry.

First and foremost, sharing knowledge accelerates progress for everyone. We have always seen scientific publications as an important component for much of our research work, but for fundamental research projects, open-source code releases and new datasets can be particularly valuable. 



They help us evaluate and contextualize the research we do in addition to stimulating new collaborations. A variety of outreach programs that continue to grow in their scope and flexibility facilitate further engagements with academia or help train the next generation of researchers: e.g. visiting faculty and faculty award programs or internships . 



Ultimately, disseminating our work and being good members of the scientific community is not just the right thing to do, it also provides many important benefits to Google.

We believe in sharing our work

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CURO ignites passion for research with student symposium

University of Georgia undergraduate students came together to showcase their individual research projects and achievements on April 8-9 at the Center for Undergraduate Research Opportunities (CURO) Symposium.

For 25 years, the CURO Symposium has served as an opportunity to highlight the breadth and depth of undergraduate research across multiple disciplines. CURO enables undergraduates to engage in faculty-mentored research as early as their first year, regardless of discipline, major, or GPA. The program supports students in discovering and seizing opportunities, selecting mentors, and showcasing and disseminating research.

“Students benefit in so many ways, including hands-on experience that goes beyond what they learn in the classroom,” CURO Program Coordinator Andrea Silletti said. “They have the ability to contribute to the body of knowledge in their chosen fields, and mentors get support for their own research endeavors while also training the next generation of scholars.”

The two-day event took place at the Classic Center and included a keynote address by Ron Walcott, vice provost for graduate education and dean of the Graduate School. Walcott also serves as a professor in the College of Agricultural and Environmental Sciences Department of Plant Pathology .

“The CURO Symposium is a lot like a seed,” Walcott said in his keynote address. “It’s like a seed that was planted 25 years ago and has positively impacted the lives of many students, whose careers have blossomed and are now producing seeds of their own.”

The 2024 Symposium featured 623 undergraduates pursuing 188 different majors from 16 schools and colleges. Almost 280 faculty members from 75 departments served as mentors. This year’s symposium featured new components, such as Cafe CURO, an opportunity for students and mentors to network over coffee, and a training session for graduate student mentors.

“This year’s symposium turned out even better than we could have imagined,” Silleti said. “We had a significant increase in the number of student presentations this year and the feedback we’ve gotten so far from participants and audience members has been fantastic.”

Cecilia Rhine is a third-year exercise and sports science student in the Mary Frances Early College of Education. Rhine’s project studied the effects of daily step counting on the strain of tibial cartilage. With a step intervention that aimed to increase participants’ daily step counts, she hypothesized that there would be a smaller decrease in cartilage thickness with greater daily steps.

“This is because I thought ‘conditioning’ of the knee would mean that the cartilage wouldn’t compress as much with more load presented on the knee,” Rhine said. “After all, it’s gotten used to having that much weight on the knee.”

After seeing her friend participate in CURO during her freshman year, Rhine wanted to do the same before she graduated.

“This journey not only provided me with valuable research experience but also fostered meaningful connections with scholars in the same field as me and supported my academic pursuits,” she said. “I know that research is an important part of kinesiology and science, which is why I wanted to be a part of it during my undergraduate studies.”

After she finishes her degree, Rhine plans to go to physical therapy school to eventually earn a Doctor of Physical Therapy license.

For Riley Forrestall, a third-year student double majoring in plant biology and ecology, this is his second time presenting at CURO. His project, titled “Comparison of Forested and Edge Communities of Flower Flies (Syrphidae),” is part of a larger study headed by CAES Department of Entomology Graduate Research Assistant Miriam Edelkind-Vealey that focuses on pollinator communities in the forest. Forrestall specifically focused on hover fly (Syrphidae) communities and how they might change between forest interior and edge communities.

“I grew up appreciating flowers and making sense of how nature is connected, so that’s why this research means so much to me,” Forrestall said.

After he graduates next year, Forrestall plans to pursue a master’s degree in evolutionary botany and entomology. Eventually, Forrestall hopes to become a professor of ecology or evolutionary biology and botany and continue his research on the origins of different species.

Thinking about the future of CURO, Silletti hopes to see an increase in presentations from non-STEM fields, such as the arts and humanities, and creating a better sense of community among UGA’s undergraduate research scholars.

“Overall,” she said, “I hope we continue to engage students across campus in projects that excite, educate, and prepare them to be successful, whatever their goals.”

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UGA hosts 2019 CURO symposium

Annual CURO Symposium to be held online April 21-24

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Research projects will partner students with DOE national labs to help students develop hands-on research experience

WASHINGTON, D.C . - Today, the U.S. Department of Energy (DOE)  announced $16 million in funding for four projects providing classroom training and research opportunities to train the next generation of accelerator scientists and engineers needed to deliver scientific discoveries. 

U.S. global competitiveness in discovery science relies on increasingly complex charged particle accelerator systems that require world-leading expertise to develop and operate. These programs will train the next generation of scientists and engineers, providing the expertise needed to lead activities supported by the DOE Office of Science. These programs will develop new curricula and guide a diverse cadre of graduate students working towards a master’s or Ph.D. thesis in accelerator science and engineering.

“Particle accelerator technology enables us to tackle challenges at the frontiers of science and benefits our nation’s high-tech industries, modern medicine, and national security,” said Regina Rameika, DOE Associate Director of Science for High Energy Physics. “The awards announced today will help to develop the workforce to advance the state-of-the-art in accelerator technology while helping deploy these technologies in commercial applications in the health, security, environmental, and industrial sectors. These programs at American universities will help ensure that our nation has a skilled and diverse workforce to develop the accelerator technology needed to meet the scientific challenges of the future.”

Research projects will partner students with DOE national labs to help students develop hands-on research experience. These projects include opportunities for graduate research across a broad range including beam physics at the systems level, technologies of large accelerators, high reliability design and failure analysis, and the fundamentals of project management. Students may also explore the material science, design methodology, fabrication techniques, and operations constraints needed to produce and operate superconducting radiofrequency accelerators. Additional research opportunities in the areas of high-reliability, high-power radiofrequency systems and large-scale cryogenic systems, particularly liquid helium systems, are available through these programs.

The projects were selected by competitive peer review under the DOE Funding Opportunity Announcement for DOE Traineeship in Accelerator Science & Engineering. 

Total funding is $16 million for projects lasting up to five years in duration, with $3 million in Fiscal Year 2024 dollars and outyear funding contingent on congressional appropriations. Funding is provided by the  High Energy Physics and the  Accelerator R&D and Production programs. The list of projects and more information can be found on the  High Energy Physics program homepage and the  Accelerator R&D and Production program homepage.

Selection for award negotiations is not a commitment by DOE to issue an award or provide funding. Before funding is issued, DOE and the applicants will undergo a negotiation process, and DOE may cancel negotiations and rescind the selection for any reason during that time. 

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NIH Extramural Nexus

Updated Analyses Suggest Continued Decline in Research Project Grant Funding Inequalities for NIH-Supported Investigators, but Organizational Inequalities Remain: FY 1998 to FY 2023

We previously showed in this January 2022 blog (based on this paper ) that the inequalities in the distribution of Research Project Grant (RPG) funding to principal investigators increased, especially at the top end of funding, during the NIH budget doubling and the first few years after the 2013 budget sequestration. The degree of inequality appeared to fall, however, after NIH implemented the Next Generation Researchers Initiative (NGRI) near the end of  FY2017. Here we present follow-up data that shows that the trends seen in recent years appear to be continuing in fiscal year (FY) 2023.

When reading this post, please keep in mind that, in general, NIH awards grants to institutions , not individual scientists. For simplicity here, we refer to PIs receiving awards, but understand this means the scientists designated by their institutions as Principal Investigators.

Measuring Inequality

Our previous post details how economists describe inequality, which we used for our analyses here. In brief:

• The “Top-proportion” allows us to track the percent of funds awarded to the top XX percent of investigators. For example, what percent of funds go to the top 1% of investigators. Were funds allocated uniformly, the top 1% of investigators would receive 1% of the funds. In an extremely unequal situation, the top 1% of investigators might receive over 50% of the funds.
• The “standard deviation of the log of funding (“SD-log”)”, by contrast, reflects more on the low and middle rungs of funding.
• The “Theil Index” is more sensitive to the higher rungs of funding, like the top-proportion approach. It enables us to explore inequalities between groups as well as those within groups.

Inequality of RPG Funding for Principal Investigators

As we saw previously, the percent of RPG funds going to the top 1% and top 10% of investigators increased during the NIH budget doubling and following the first years after budget increases following the FY2013 budget sequestration (Figure 1, panels A and B). After the announcement of NGRI late in FY2017 , however, the degree of inequality fell. We can see that in FY 2023 the percent of funds going to the top 1% of investigators dropped below FY2013 levels (Figure 1, panel B). The Theil index showed a similar trajectory (Figure 1, panel D). we did not see any clear trends in the SD-log (Figure 1, panel C), suggesting no changes seen for researchers at the lower or middle rungs of funding.

The FY 2023 data also demonstrate similar characteristics for the top 1% of funded investigators as seen before. They are more likely to be in late career stages (Panel A), male (Panel B), white (Panel C), hold an MD degree (Panel D), and supported by multiple RPG awards (see also Table 1).

Table 1: Investigator Characteristics According to Centile of Funding in Fiscal Year 2023. Values shown in parentheses are percentages for categorical variables and IQR for continuous variables. IQR = inter-quartile range. ND = not displayed due to small cell size.

Inequality Between and Within Groups

Next, we sought to understand if the inequalities seen were “within-group” or “between-group.” As a reminder from our prior post, we can consider height inequalities of athletes to conceptualize this concept. For instance, there is a great deal of “between-group” inequalities in the heights of jockeys and professional basketball players. On the other hand, we would primarily observe “within-group” inequalities if we focused on professional basketball players from either the east or west coast.

Focusing our attention back on NIH supported scientists categorized by career stage, we can use Theil index to assess these types of inequalities. A visual look at the distribution of funding for these PIs in FY 2023 shows the inequalities are primarily within- group for gender, race-ethnicity, and degree (Figure 2).

Organizational Inequalities

While investigator inequalities declined since FY2017 (Figure 1, panels A, B, and D), we saw an increase in organizational inequalities over the same time (Figure 3, Panels A, B, and D). The degree of organizational inequality was still lower in FY2023 than when it peaked in the late 2000s.

There were also between-group inequalities, with medical schools receiving substantially more funding than other kinds of institutions (Figure 4).

Our follow-up (FY 2023) analyses continue to show that investigator inequalities substantially declined since NGRI. However, organizational inequalities modestly increased during the same time. We will continue to follow these data to better understand how our programs and policies can help us reduce funding inequalities when possible.

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Cambridge college cuts ties with philosophy fellow who sparked race row

A college at the University of Cambridge has cut ties with a philosophy fellow who sparked a race row.

Nathan Cofnas , an early career research fellow in the Faculty of Philosophy, is reported to have had his research affiliation with Emmanuel College terminated.

The lecturer had said that in a meritocracy, “blacks would disappear from almost all high-profile positions outside of sports and entertainment” and dismissed racial equality as “based on lies”.

In a controversial blog post, he added: “In a meritocracy, Harvard faculty would be recruited from the best of the best students, which means the number of black professors would approach 0 per cent.”

According to Varsity, Cambridge’s student newspaper, Emmanuel’s Faculty of Philosophy told Mr Cofnas in a letter on April 5 that it had decided to end its relationship with him.

“The committee first considered the meaning of the blog and concluded that it amounted to, or could reasonably be construed as amounting to, a rejection of diversity, equality and inclusion policies,” the newspaper quoted the letter as saying.

“The committee concluded that the core mission of the college was to achieve educational excellence and that diversity and inclusion were inseparable from that. The ideas promoted by the blog therefore represented a challenge to the college’s core values and mission.”

‘Commitment to freedom’

When the blog posts emerged in February, Doug Chalmers, master of the college, told students that “we retain our commitment to freedom of thought and expression” and accepted Mr Cofnas’s “academic right, as enshrined by law, to write about his views”.

The master added: “Were the University of Cambridge to dismiss Cofnas, it would sound a warning to students and academics everywhere: when it comes to controversial topics, even the world’s most renowned universities can no longer be relied upon to stand by their commitment to defend freedom of thought and discussion.”

But Lord Woolley, the principal of Cambridge’s Homerton College, told students: “I see it for what it is. Abhorrent racism, masquerading as pseudo-intellect... There is no place for bigots in institutions like this.”

Students protested against the fellow remaining on the college payroll and 1,200 people signed a petition in 2022, when he was first appointed, to sack him from his post, which is funded by the Leverhulme Trust.

That came after a separate row over a 2019 article by Mr Cofnas claiming that there were “gaps” in IQ between different racial groups.

Prof Bhaskar Vira, Cambridge’s pro-vice-chancellor for education, said in February that “everyone at Cambridge has earned their place on merit”, in response to the row.

Founded in 1584, Emmanuel College counts the novelist Hugh Walpole, the mathematician John Wallis and John Harvard, one of the founders of Harvard College, among its alumni.

The Telegraph has contacted Emmanuel College and Mr Cofnas for comment.

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