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Found 59 papers, 1 papers with code
Explaining Image Classifiers
We focus on explaining image classifiers, taking the work of Mothilal et al. [2021] (MMTS) as our point of departure.
Subjective Causality
We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions.
Mathematical Explanations
A definition of what counts as an explanation of mathematical statement, and when one explanation is better than another, is given.
Inference for Probabilistic Dependency Graphs
Probabilistic dependency graphs (PDGs) are a flexible class of probabilistic graphical models, subsuming Bayesian Networks and Factor Graphs.
Joint Behavior and Common Belief
For over 25 years, common belief has been widely viewed as necessary for joint behavior.
Causal Models with Constraints
The goal of this paper is to extend standard causal models to allow for constraints on settings of variables.
A Causal Analysis of Harm
In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality (Halpern, 2016).
Quantifying Harm
In a companion paper (Beckers et al. 2022), we defined a qualitative notion of harm: either harm is caused, or it is not.
From Outcome-Based to Language-Based Preferences
We review the literature on models that try to explain human behavior in social interactions described by normal-form games with monetary payoffs.
The Benefits of Coarse Preferences
We study the strategic advantages of coarsening one's utility by clustering nearby payoffs together (i. e., classifying them the same way).
The brain as a probabilistic transducer: an evolutionarily plausible network architecture for knowledge representation, computation, and behavior
The brain in our abstract model is a network of nodes and edges.
Reasoning About Causal Models With Infinitely Many Variables
Generalized structural equations models (GSEMs) [Peters and Halpern 2021], are, as the name suggests, a generalization of structural equations models (SEMs).
Causal Modeling With Infinitely Many Variables
Structural-equations models (SEMs) are perhaps the most commonly used framework for modeling causality.
Security Properties as Nested Causal Statements
We moreover revisit some design decisions of the HP framework that were made with non-nested causal statements in mind, such as the choice to treat specific values of causal variables as opposed to the variables themselves as causes, and may no longer be appropriate for nested ones.
Bounded Rationality in Las Vegas: Probabilistic Finite Automata PlayMulti-Armed Bandits
While traditional economics assumes that humans are fully rational agents who always maximize their expected utility, in practice, we constantly observe apparently irrational behavior.
Combining the Causal Judgments of Experts with Possibly Different Focus Areas
In this work we show how causal models can be combined in cases where the experts might disagree on the causal structure for variables that appear in both models due to having different focus areas.
Combining Experts' Causal Judgments
We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention.
Information Acquisition Under Resource Limitations in a Noisy Environment
We introduce a theoretical model of information acquisition under resource limitations in a noisy environment.
MDPs with Unawareness in Robotics
Doing this results in a family of systems, each of which has an extremely large action space, although only a few actions are "interesting".
Causality, Responsibility and Blame in Team Plans
Many objectives can be achieved (or may be achieved more effectively) only by a group of agents executing a team plan.
A Conceptually Well-Founded Characterization of Iterated Admissibility Using an "All I Know" Operator
In earlier work, we gave a characterization of iterated admissibility using an "all I know" operator, that captures the intuition that "all the agent knows" is that agents satisfy the appropriate rationality assumptions.
Approximate Causal Abstraction
Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system.
Blameworthiness in Multi-Agent Settings
We provide a formal definition of blameworthiness in settings where multiple agents can collaborate to avoid a negative outcome.
Abstracting Causal Models
We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the "right" choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions.
Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility
We provide formal definitions of degree of blameworthiness and intention relative to an epistemic state (a probability over causal models and a utility function on outcomes).
An Epistemic Foundation for Authentication Logics (Extended Abstract)
We present a simple logic based on the well-understood modal operators of knowledge, time, and probability, and show that it is able to handle issues that have often been swept under the rug by other approaches, while being flexible enough to capture all the higher- level security notions that appear in BAN logic.
Translucent Players: Explaining Cooperative Behavior in Social Dilemmas
We show that by assuming translucent players, we can recover many of the regularities observed in human behavior in well-studied games such as Prisoner's Dilemma, Traveler's Dilemma, Bertrand Competition, and the Public Goods game.
Why Bother With Syntax?
This short note discusses the role of syntax vs. semantics and the interplay between logic, philosophy, and language in computer science and game theory.
A Modification of the Halpern-Pearl Definition of Causality
The original Halpern-Pearl definition of causality [Halpern and Pearl, 2001] was updated in the journal version of the paper [Halpern and Pearl, 2005] to deal with some problems pointed out by Hopkins and Pearl [2003].
An Introduction to Logics of Knowledge and Belief
This chapter provides an introduction to some basic concepts of epistemic logic, basic formal languages, their semantics, and proof systems.
Minimizing Regret in Dynamic Decision Problems
The menu-dependent nature of regret-minimization creates subtleties when it is applied to dynamic decision problems.
Appropriate Causal Models and the Stability of Causation
It is also shown that, by adding extra variables, a modification to the original HP definition made to deal with an example of Hopkins and Pearl may not be necessary.
Cause, Responsibility, and Blame: oA Structural-Model Approach
Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent.
The Computational Complexity of Structure-Based Causality
Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X=x is a cause of Y=y is NP-complete in binary models (where all variables can take on only two values) and\ Sigma_2^P-complete in general models.
A Logic for Reasoning about Upper Probabilities
We present a propositional logic to reason about the uncertainty of events, where the uncertainty is modeled by a set of probability measures assigning an interval of probability to each event.
Axiomatizing Causal Reasoning
Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here.
MDPs with Unawareness
We define a new framework, MDPs with unawareness (MDPUs) to deal with the possibilities that a DM may not be aware of all possible actions.
Conditional Plausibility Measures and Bayesian Networks
A general notion of algebraic conditional plausibility measures is defined.
A Logic for Reasoning about Evidence
We introduce a logic for reasoning about evidence, that essentially views evidence as a function from prior beliefs (before making an observation) to posterior beliefs (after making the observation).
A Game-Theoretic Analysis of Updating Sets of Probabilities
We consider how an agent should update her uncertainty when it is represented by a set P of probability distributions and the agent observes that a random variable X takes on value x, given that the agent makes decisions using the minimax criterion, perhaps the best-studied and most commonly-used criterion in the literature.
When Ignorance is Bliss
It is commonly-accepted wisdom that more information is better, and that information should never be ignored.
Updating Probabilities
A criterion known as CAR (coarsening at random) in the statistical literature characterizes when ``naive' conditioning in a naive space works.
Defining Relative Likelihood in Partially-Ordered Preferential Structures
Starting with a likelihood or preference order on worlds, we extend it to a likelihood ordering on sets of worlds in a natural way, and examine the resulting logic.
Evidence with Uncertain Likelihoods
An agent often has a number of hypotheses, and must choose among them based on observations, or outcomes of experiments.
Making Decisions Using Sets of Probabilities: Updating, Time Consistency, and Calibration
We consider how an agent should update her beliefs when her beliefs are represented by a set P of probability distributions, given that the agent makes decisions using the minimax criterion, perhaps the best-studied and most commonly-used criterion in the literature.
A logic for reasoning about ambiguity
Standard models of multi-agent modal logic do not capture the fact that information is often \emph{ambiguous}, and may be interpreted in different ways by different agents.
Compact Representations of Extended Causal Models
Judea Pearl was the first to propose a definition of actual causation using causal models.
Weighted regret-based likelihood: a new approach to describing uncertainty
In this paper, a notion of comparative likelihood when uncertainty is represented by a weighted set of probability measures is defined.
Graded Causation and Defaults
Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality.
Decision Theory with Resource-Bounded Agents
There have been two major lines of research aimed at capturing resource-bounded players in game theory.
The Relationship between Knowledge, Belief and Certainty
We consider the relation between knowledge and certainty, where a fact is known if it is true at all worlds an agent considers possible and is certain if it holds with probability 1.
A New Approach to Updating Beliefs
An alternate way of understanding our definition of conditional belief is provided by considering ideas from an earlier paper [Fagin and Halpern, 1989], where we connect belief functions with inner measures.
Causes and Explanations: A Structural-Model Approach --- Part 1: Causes
We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definitions yield a plausible and elegant account ofcausation that handles well examples which have caused problems forother definitions and resolves major difficulties in the traditionalaccount.
From Causal Models To Counterfactual Structures
Galles and Pearl claimed that "for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis's [possible-worlds] framework."
Updating Sets of Probabilities
There are several well-known justifications for conditioning as the appropriate method for updating a single probability measure, given an observation.
Constructive Decision Theory
In most contemporary approaches to decision making, a decision problem is described by a sets of states and set of outcomes, and a rich set of acts, which are functions from states to outcomes over which the decision maker (DM) has preferences.
