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.

In this paper, I examine decision making in an environment where payoff relevant contingencies are vague, that is neither absolutely true nor absolutely false. In this model, an agent values acts that are predicated on linguistic statements, rather than an exogenous state-space. I axiomatize a class of preferences under which agent’s beliefs about the degree of truth of contingencies is identified from her choices. I then apply this model to a simple contracting environment wherein contracts must be explicitly constructed using said linguistic statements. I show that different restrictions on the contract writing technology can impart different outcomes. However, under mild conditions, as the cost of contractual complexity vanishes, so do the distortionary effects of vagueness.

Without the assumption of complete, shared awareness, it is necessary to consider communication between agents who may entertain different representations of the world. A syntactic (language-based) approach provides powerful tools to address this problem. In this paper, we define translation operators between two languages which provide a “best approximation” for the meaning of propositions in the target language subject to its expressive power. We show that, in general, the translation operators preserve some, but not all, logical operations. We derive necessary and sufficient conditions for the existence of a joint state space and a joint language, in which the subjective state spaces of each agent, and their individual languages, may be embedded. This approach allows us to compare languages with respect to their expressiveness and thus, with respect to the properties of the associated state space.

I examine how a decision maker can incentivize an expert to reveal novel actions, expanding the set from which he can choose, without making ex-ante commitments regarding as-of-yet unrevealed actions. The outcomes achievable by any (incentive compatible) mechanism are characterized by the iterated revelation protocol: a simple dynamic interaction where, each round, the expert reveals novel actions and the decision maker adds actions to a shortlist; when nothing novel is revealed, the mechanism ends with the expert choosing an action from the shortlist. Greedy strategies – where the decision maker optimizes myopically – delineate the decision maker's maximal payoff achievable by any efficient mechanism.

This paper considers an image-conscious decision maker (DM) who cares not only about the direct consequences of his actions, but also about how these actions can be psychologically rationalized—are his actions considered generous, patient, sophisticated, etc.? image-consciousness nests both shame-driven preferences (wanting to conceal information) and signaling (wanting to reveal information; e.g., conspicuous consumption). A specific type of image-consciousness is norm driven behavior whereby the DM derives additional utility whenever his choices are consistent with a set of prescribed norms. The notion of consistency determines the DM’s attitude towards information revelation. This paper axiomatizes the behavior of an image/norm concerned DM and identifies the DM’s value to inducing an image or adhering to a norm.

We show that it is possible to understand and identify a decision maker’s subjective causal judgements by observing her preferences over interventions. Following Pearl [2000, DOI: doi.org/10.1017/S0266466603004109 ], we represent causality using causal models (also called structural equations models), where the world is described by a collection of variables, related by equations. We show that if a preference relation over interventions satisfies certain axioms (related to standard axioms regarding counterfactuals), then we can define (i) a causal model, (ii) a probability capturing the decision-maker’s uncertainty regarding the external factors in the world and (iii) a utility on outcomes such that each intervention is associated with an expected utility and such that intervention A is preferred to B iff the expected utility of A is greater than that of B. In addition, we characterize when the causal model is unique. Thus, our results allow a modeler to test the hypothesis that a decision maker’s preferences are consistent with some causal model and to identify causal judgements from observed behavior.

We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions. Following Pearl [2000], we represent causality using causal models (also called structural equations models), where the world is described by a collection of variables, related by equations. We show that if a preference relation over interventions satisfies certain axioms (related to standard axioms regarding counterfactuals), then we can define (i) a causal model, (ii) a probability capturing the decision-maker's uncertainty regarding the external factors in the world and (iii) a utility on outcomes such that each intervention is associated with an expected utility and such that intervention $A$ is preferred to $B$ iff the expected utility of $A$ is greater than that of $B$. In addition, we characterize when the causal model is unique. Thus, our results allow a modeler to test the hypothesis that a decision maker's preferences are consistent with some causal model and to identify causal judgements from observed behavior.

This paper puts forth a class of algebraic structures, relativized Boolean algebras (RBAs), that provide semantics for propositional logic in which truth/validity is only defined relative to a local domain. In particular, the join of an event and its complement need not be the top element. Nonetheless, behavior is locally governed by the laws of propositional logic. By further endowing these structures with operators -- akin to the theory of modal Algebras -- RBAs serve as models of modal logics in which truth is relative. In particular, modal RBAs provide semantics for various well known awareness logics and an alternative view of possibility semantics.

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We study the effects of exposure to unawareness on risk preferences using a novel experimental task. The task has solutions that are difficult to find, but easy to verify and so exposes subjects to unawareness in a natural way. We find that increased exposure to unawareness alone does not affect risk taking. The role of context, however, is shown to be important. For treatments inducing higher unawareness, subjects are more risk averse when the risk elicitation task is framed in the same context as the unawareness-inducing task versus framed in a neutral way; we observe no such differences for the control treatment. We propose a novel decision theoretic model that guides the interpretation of the experimental findings. Our results could inform the decision and game-theoretic literatures, as most models of unawareness assume risk preferences are orthogonal to varying awareness.

. Awareness growth—coming to entertain propositions of which one was previously unaware—is a crucial aspect of epistemic thriving. And yet, it is widely believed that orthodox Bayesianism cannot accommodate this phenomenon, since that would require employing supposedly defective catch-all propositions. Orthodox Bayesianism, it is concluded, must be amended. In this paper, I show that this argument fails, and that, on the contrary, the orthodox version of Bayesianism is particularly well-suited to accommodate awareness growth. For it entails what I call the refinement view , which allows us to capture that awareness growth consists in the increase of one’s capacity of discernment. 0

I examine the structure of random choice resulting from random expected utility maximization and a tie-breaking rule. I provide a partial identification result, characterizing the set of random expected utility models that could have generated the observed choice frequencies. I then consider a particular class of random choice rules for which it is possible to constructively identify the consistent random utility model that produces indifference the least. These random choice rules are characterized by breaking ties in favor of strict convex combinations. Towards proving these results, I introduce and axiomatize the notion of a choice capacity, representing the frequency of choice by strict maximization. Choice capacities, while not necessarily observable themselves, provide the technical machinery to translate arbitrary random expected utility models into choice behavior.

In dynamic settings each economic agent's choices can be revealing of her private information. This elicitation via the rationalization of observable behavior depends each agent's perception of which payoff-relevant contingencies other agents persistently deem as impossible. We formalize the potential heterogeneity of these perceptions as disagreements at higher-orders about the set of payoff states of a dynamic game. We find that apparently negligible disagreements greatly affect how agents interpret information and assess the optimality of subsequent behavior: When knowledge of the state space is only 'almost common', strategic uncertainty may be greater when choices are rationalized than when they are not--forward and backward induction predictions, respectively, and while backward induction predictions are robust to small disagreements about the state space, forward induction predictions are not. We also prove that forward induction predictions always admit unique selections a la Weinstein and Yildiz (2007) (also for spaces not satisfying richness) and backward induction predictions do not.

I propose a normative updating rule, extended Bayesianism, for the incorporation of probabilistic information arising from the process of becoming more aware. Extended Bayesianism generalizes standard Bayesian updating to allow the posterior to reside on richer probability space than the prior. I then provide a behavioral characterization of this rule to conclude that a decision maker's subjective expected utility beliefs are consistent with extended Bayesianism.

In this paper, we provide a theoretical framework to analyze an agent who misinterprets or misperceives the true decision problem she faces. Within this framework, we show that a wide range of behavior observed in experimental settings manifest as failures to perceive implications, in other words, to properly account for the logical relationships between various payoff relevant contingencies. We present behavioral characterizations corresponding to several benchmarks of logical sophistication and show how it is possible to identify which implications the agent fails to perceive. Thus, our framework delivers both a methodology for assessing an agent's level of contingent thinking and a strategy for identifying her beliefs in the absence full rationality.

This paper provides a model to analyze and identify a decision maker's (DM's) hypothetical reasoning. Using this model, I show that a DM's propensity to engage in hypothetical thinking is captured exactly by her ability to recognize implications (i.e., to identify that one hypothesis implies another) and that this later relation is encoded by a DM's observable behavior. Thus, this characterization both provides a concrete definition of (flawed) hypothetical reasoning and, importantly, yields a methodology to identify these judgments from standard economic data.

We investigate how to model the beliefs of an agent who becomes more aware. We use the framework of Halpern and Rego (2013) by adding probability, and define a notion of a model transition that describes constraints on how, if an agent becomes aware of a new formula φ in state s of a model M, she transitions to state s* in a model M*. We then discuss how such a model can be applied to information disclosure.

We note that in environments such as exploration problems, in which agents have to choose a single action out of several in each period, an agent's preferences over different strategies can only reveal the margins of her beliefs. However, classical notions of Bayesian updating regard the joint distribution. We develop the relevant environment and tools to solve this issue: We introduce a necessary and sufficient condition on the margins of an agent's beliefs to be consistent with an exchangeable process. Such a consistent process is typically not unique; contemporaneous correlation cannot be identified. We conclude that contemporaneous correlations do not affect the optimal strategy in classical bandit problems.

We develop a modal logic to capture partial awareness. The logic has three building blocks: objects, properties, and concepts. Properties are unary predicates on objects; concepts are Boolean combinations of properties. We take an agent to be partially aware of a concept if she is aware of the concept without being aware of the properties that define it. The logic allows for quantification over objects and properties, so that the agent can reason about her own unawareness. We then apply the logic to contracts, which we view as syntactic objects that dictate outcomes based on the truth of formulas. We show that when agents are unaware of some relevant properties, referencing concepts that agents are only partially aware of can improve welfare.