About

Joseph Halpern was a professor of computer science and held the position of Joseph C. Ford Chair of Engineering at Cornell University. His research focused on the interface between game and decision theory and computer science, reasoning about knowledge and uncertainty, and causality. He also did work on security, fault-tolerant distributed computing, and modal logic. His work was at the boundary of a number of fields, and he was recognized for his outstanding contributions with numerous awards, including being an IEEE Fellow, an ACM Fellow, and a member of the American Academy of Arts and Sciences. Halpern was described as a towering computer scientist and mentor, and his interdisciplinary approach often involved engaging with fields such as economics and philosophy.

Research topics

• Psychology
• Cognitive psychology
• Artificial Intelligence
• Computer Science
• Economics
• Microeconomics
• Social psychology
• Sociology
• Epistemology
• Econometrics
• Positive economics
• Mathematical economics

Selected publications

• Unintended Consequences: Updating Causal Models

  ArXiv.org · 2026-03-10

  articleOpen access1st authorCorresponding

  We examine how causal beliefs affect an agent's choices and how feedback on those choices leads to updated causal beliefs. Building on the structural-equations framework for modeling causality, we first examine the general problem of updating causal beliefs in the face of novel (and possibly inexplicable) data. We model an agent who is uncertain of the true causal model, and therefore entertains a probabilistic belief over the set of possible models. We then consider how causal beliefs influence choices by building a model of agency and utility on top of the usual structural-equations framework. Using these two components, we propose a notion of steady state, where the feedback received from an agent's optimal action, given her current beliefs about the true causal model, can be rationalized by those beliefs.

  PublisherOA PDF

• Unintended Consequences: Updating Causal Models

  arXiv (Cornell University) · 2026-03-10

  preprintOpen access1st authorCorresponding

  We examine how causal beliefs affect an agent's choices and how feedback on those choices leads to updated causal beliefs. Building on the structural-equations framework for modeling causality, we first examine the general problem of updating causal beliefs in the face of novel (and possibly inexplicable) data. We model an agent who is uncertain of the true causal model, and therefore entertains a probabilistic belief over the set of possible models. We then consider how causal beliefs influence choices by building a model of agency and utility on top of the usual structural-equations framework. Using these two components, we propose a notion of steady state, where the feedback received from an agent's optimal action, given her current beliefs about the true causal model, can be rationalized by those beliefs.

  PublisherDOI

• Tracking Truth with Liquid Democracy

  Management Science · 2025-01-08 · 2 citations

  article

  The dynamics of random transitive delegations on a graph are of particular interest when viewed through the lens of an emerging voting paradigm: liquid democracy. This paradigm allows voters to choose between directly voting and transitively delegating their votes to other voters so that those selected cast a vote weighted by the number of delegations that they received. In the epistemic setting, where voters decide on a binary issue for which there is a ground truth, previous work showed that a few voters may amass such a large amount of influence that liquid democracy is less likely to identify the ground truth than direct voting. We quantify the amount of permissible concentration of power and examine more realistic delegation models, showing that they behave well by ensuring that (with high probability) there is a permissible limit on the maximum number of delegations received. Our theoretical results demonstrate that the delegation process is similar to well-known processes on random graphs that are sufficiently bounded for our purposes. Along the way, we prove new bounds on the size of the largest component in an infinite Pólya urn process, which may be of independent interest. In addition, we empirically validate the theoretical results, running six experiments (for a total of N = 168 participants, 62 delegation graphs, and over 11,000 votes collected). We find that empirical delegation behaviors meet the conditions for our positive theoretical guarantees. Overall, our work alleviates concerns raised about liquid democracy and bolsters the case for the applicability of this emerging paradigm. This paper was accepted by Martin Bichler, market design, platform, and demand analytics. Funding: This work was supported by the Michael Hammer Fellowship, the Office of Naval Research [2016 Vannevar Bush Faculty Fellowship, 2020 ONR Vannevar Bush Faculty Fellowship, and Grant N00014-20-1-2488], the Office of Secretary of Defense [Grants ARO MURI W911NF-19-0217 and ARO W911NF-17-1-0592], Simons [Investigator Award 622132], the Open Philanthropy Foundation, and the National Science Foundation [Grants IIS-178108, CCF-1733556, CCF-1918421, CCF-2007080, IIS-1703846, and IIS-2024287]. J. Y. Halpern was supported by a grant from the Cooperative AI Foundation [ARO Grant W911NF-22-1-0061]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02470 .

  PublisherDOI

• Causality Without Causal Models

  Electronic Proceedings in Theoretical Computer Science · 2025-11-25

  articleOpen access1st authorCorresponding

  Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl.It is defined using causal models (also known as structural equations models).We abstract the definition, extracting its key features, so that it can be applied to any other model where counterfactuals are defined.By abstracting the definition, we gain a number of benefits.Not only can we apply the definition in a wider range of models, including ones that allow, for example, backtracking, but we can apply the definition to determine if A is a cause of B even if A and B are formulas involving disjunctions, negations, beliefs, and nested counterfactuals (none of which can be handled by the Halpern-Pearl definition).Moreover, we can extend the ideas to getting an abstract definition of explanation that can be applied beyond causal models.Finally, we gain a deeper understanding of features of the definition even in causal models.

  PublisherOA PDFDOI

• Evolution of diverse (and advanced) cognitive abilities through adaptive fine-tuning of learning and chunking mechanisms

  Philosophical Transactions of the Royal Society B Biological Sciences · 2025-06-26

  preprintOpen accessSenior author

  The evolution of cognition is frequently discussed as the evolution of cognitive abilities or the evolution of some neuronal structures in the brain. However, since such traits or abilities are often highly complex, understanding their evolution requires explaining how they could have gradually evolved through selection acting on heritable variations in simpler cognitive mechanisms. With this in mind, making use of a previously proposed theory, here, we show how the evolution of cognitive abilities can be captured by the fine-tuning of basic learning mechanisms and, in particular, chunking mechanisms. We use the term chunking broadly for all types of non-elemental learning, claiming that the process by which elements are combined into chunks and associated with other chunks, or elements, is critical for what the brain can do, and that it must be fine-tuned to ecological conditions. We discuss the relevance of this approach to studies in animal cognition, using examples from animal foraging and decision-making, problem-solving and cognitive flexibility. Finally, we explain how even the apparent human–animal gap in sequence learning ability can be explained in terms of different fine-tunings of a similar chunking process. This article is part of the Theo Murphy meeting issue ‘Selection shapes diverse animal minds’.

  PublisherDOI

• Qualitative Mechanism Independence

  ArXiv.org · 2025-01-26

  preprintOpen accessSenior author

  We define what it means for a joint probability distribution to be compatible with a set of independent causal mechanisms, at a qualitative level -- or, more precisely, with a directed hypergraph ${\mathcal{A}}$, which is the qualitative structure of a probabilistic dependency graph (PDG). When ${\mathcal{A}}$ represents a qualitative Bayesian network, QIM-compatibility with ${\mathcal{A}}$ reduces to satisfying the appropriate conditional independencies. But giving semantics to hypergraphs using QIM-compatibility lets us do much more. For one thing, we can capture functional dependencies. For another, we can capture important aspects of causality using compatibility: we can use compatibility to understand cyclic causal graphs, and to demonstrate structural compatibility, we must essentially produce a causal model. Finally, QIM-compatibility has deep connections to information theory. Applying our notion to cyclic structures helps to clarify a longstanding conceptual issue in information theory.

  PublisherOA PDFDOI

• Intents in Actions

  2025-06-16

  articleOpen access1st authorCorresponding

  What outcomes does an agent intend in performing an action? We develop a principled approach to this question and provide a new formal definition of intent in a causal framework. Our definition is modular, draws on ideas from philosophy of law, and works in many natural cases where earlier proposed definitions did not.

  PublisherDOI

• A Unifying Framework for Causal Modeling With Infinitely Many Variables

  Journal of Artificial Intelligence Research · 2025-08-24

  articleOpen accessSenior author

  Structural-equations models (SEMs) are perhaps the most commonly used framework for modeling causality, but they do not capture all domains of interest. For example, dynamical systems that evolve in continuous time are an important class of domains that are not (naturally) captured by SEMs. A wide variety of approaches have been proposed to fill the gap, including dynamical structural causal models (Bongers, Blom and Mooij 2018), causal constraints models (Blom, Bongers and Mooij 2019), and counterfactual resimulation (Laurent, Yang, and Fontana 2018). These models complement common-sense causal interpretations of specific dynamical systems, such as systems of ODEs. All these approaches look quite different from each other and from SEMs. They are hard to compare, and concepts developed for one approach may not make sense for another. But they are capturing the same notion of causality as SEMs do, in the sense that interventions map to outcomes. We propose a class of models that are, in a certain natural sense, the most expressive generalization of SEMs. Our generalized SEMs (GSEMs) can be viewed as a unifying framework that recovers structural dynamical causal models, causal constraints models, counterfactual resimulation, and common-sense causal interpretations of systems of ODEs and hybrid automata (Alur et al. 1992) as special cases. The input-output behavior, or “interface”, of GSEMs is exactly that of SEMs, which means that definitions of concepts like actual cause, responsibility, blame, and explanation, can be immediately lifted from SEMs to GSEMs. The generality of GSEMs also makes them ideally suited to studying causality in the abstract; for example, they have been used to establish independence relationships among Halpern’s axioms for SEMs (Peters and Halpern 2022).

  PublisherOA PDFDOI

• A Knowledge-Based Analysis of Intersection Protocols

  arXiv (Cornell University) · 2024-01-01

  preprintOpen access

  The increasing wireless communication capabilities of vehicles creates opportunities for more efficient intersection management strategies. One promising approach is the replacement of traffic lights with a system wherein vehicles run protocols among themselves to determine right of way. In this paper, we define the intersection problem to model this scenario abstractly, without any assumptions on the specific structure of the intersection or a bound on the number of vehicles. Protocols solving the intersection problem must guarantee safety (no collisions) and liveness (every vehicle eventually goes through). In addition, we would like these protocols to satisfy various optimality criteria, some of which turn out to be achievable only in a subset of the contexts. In particular, we show a partial equivalence between eliminating unnecessary waiting, a criterion of interest in the distributed mutual-exclusion literature, and a notion of optimality that we define called lexicographical optimality. We then introduce a framework to design protocols for the intersection problem by converting an intersection policy, which is based on a global view of the intersection, to a protocol that can be run by the vehicles through the use of knowledge-based programs. Our protocols are shown to guarantee safety and liveness while also being optimal under sufficient conditions on the context. Finally, we investigate protocols in the presence of faulty vehicles that experience communication failures and older vehicles with limited communication capabilities. We show that intersection protocols can be made safe, live and optimal even in the presence of faulty behavior.

  PublisherOA PDFDOI

• Communication games, sequential equilibrium, and mediators

  Journal of Economic Theory · 2024-08-14 · 1 citations

  articleSenior author
  PublisherDOI

Recent grants

• III: Large: Causal Databases

  NSF · $2.4M · 2009–2016

• SGER: Decision-making In Complex Systems

  NSF · $90k · 2000–2002

• RI: Small: Towards a Formal Theory of Blameworthiness, Intention, and Moral Responsibility

  NSF · $427k · 2017–2021

• RI: Medium: Computation, Language, and Games

  NSF · $1.2M · 2017–2023

• Towards Improved Logics For Reasoning About Security

  NSF · $300k · 2002–2006

Frequent coauthors

• Valerio Capraro

  University of Milano-Bicocca

  80 shared

• Matjaž Perc

  China Medical University

  75 shared

• Rafael Pass

  58 shared

• Ronald Fagin

  42 shared

• Moshe Y. Vardi

  34 shared

• Christopher Hitchcock

  33 shared

• Riccardo Pucella

  32 shared

• Hana Chockler

  31 shared

Awards & honors

• IEEE Fellow Institute of Electrical and Electronics Engineer…
• John Simon Guggenheim Memorial Foundation Guggenheim Fellows…
• European Association for Theoretical Computer Science Gödel…
• U.S. Department of State Fulbright Scholar (2002)
• American Academy of Arts and Sciences Member (2015)