Working papers

A decision maker (DM) defines alternatives as a set of features (namely, measurable characteristics). An external observer sees only the DM’s choices from various feasible sets of features but lacks information about which features the DM attends to, how they are evaluated, or what decision procedure is used. Under what conditions can the observer identify the subset of features that matter to the DM? Our approach relies on relatively weak behavioural assumptions—notably, that the DM does not choose options that are Pareto dominated with respect to the relevant features. We characterise which collections of choice–feasible set pairs are informative about the DM’s underlying representation. Our results apply in environments with minimal structure—without requiring functional form assumptions or price data—and can accommodate settings where even only a single choice is observed. Choices are shaped by a variety of factors, including preferences, attention, and cognitive ability. These factors generate a multiplicity of unobserved behavioral types. We study environments in which the aggregate choices observed by the analyst arise from the unobserved choices of such types. A central question is when both the distribution of types and their choice patterns can be inferred from aggregate data. We develop new identification results establishing conditions under which such inference is possible. All our conditions formalize a common idea: the distribution and behavior of types are identifiable whenever the data are consistent with sufficiently heterogeneous behavior across types, defined precisely within our framework. Blackwell’s theorem relates the value of information to the “informativeness” of the information structure. His analysis applies to decision makers who are expected utility maximizers and know the information structure of the decision problem. When decision makers do not know the information structure precisely, the signal generating process and the posterior distributions are often only partially known. This paper studies preferences of decision makers with partial knowledge about signals and posterior probability distributions. The partial information approach allows us to relate the value of information to the decision maker’s attitude towards ambiguity. We introduce a new concept of informativeness based on the centroid and prove a theorem in the spirit of Blackwell. Furthermore, we characterize the value of information in terms of the preference relation over information structures. Depending on ambiguity attitude the value of information may be negative.​ ​

Publications

​​Testing negative value of information and ambiguity aversion, 2023, Journal of Economic Theory 213: 105730 (with Christopher Kops)

The standard Subjective Expected Utility model of decision-making implies that information can never have a negative value ex-ante. Many ambiguity theories have since questioned this property. We provide an experimental test of the connection between the value of information and ambiguity attitude. Our results show that the value of information can indeed be negative when new information renders hedging against ambiguity impossible. Moreover, the value of information is correlated with ambiguity aversion. This confirms the predictions from ambiguity theories and may have implications for decision-making in uncertain and dynamic environments. Neither complexity avoidance nor information with ambiguous reliability can reproduce the results.

​​Decision-making with partial information, 2021, Journal of Economic Theory 198: 105369. (with  Jürgen Eichberger)
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In this paper, we study choice under uncertainty with belief functions. Belief functions can capture partial information by describing what is objectively known about the probabilities of events. State-contingent acts together with a belief function over states induce belief functions over outcomes. We assume that decision makers have preferences over belief functions that reflect both their valuation of outcomes and the information available about the likelihood of outcomes. We provide axioms characterizing a preference representation for belief functions that captures what is (objectively) known about the likelihood of outcomes and combines it with subjective beliefs according to the “principle of insufficient reason” whenever the likelihood of events is unknown. This treatment of partial information yields a natural distinction between ambiguity and ambiguity attitudes. The approach is novel in its treatment of partial information and in its axiomatization of the uniform distribution in case of ignorance.


​​Kolmogorov Consistency Theorem for nonstochastic random processes, 2019, Sankhya 81: 399–405. (with Victor Ivanenko)

Stochastic random phenomena studied in probability theory constitute only a part of all random phenomena, as was pointed out by Borel (1956) and Kolmogorov (1986). The need to study nonstochastic randomness led to new models. In particular, Ivanenko and Labkovsky (Sankhya A 77, 2, 237–248. 2015) defined a set of finitely additive probability measures as a set of accumulation points of a sequence or a net of frequency distributions. Here we prove the existence theorem for a nonstochastic random process described by a system of weak* closed sets of finite-dimensional distributions. Concretely, we show that a corresponding system of random variables can be defined on a probability space with a probability measure determined up to some set of measures, provided that the sets of finite-dimensional distributions are consistent.


Expected utility for nonstochastic risk, 2017, Mathematical Social Sciences 86: 18–22. (with Victor Ivanenko)

Stochastic random phenomena considered in von Neumann–Morgenstern utility theory constitute only a part of all possible random phenomena (Kolmogorov, 1986). We show that any sequence of observed consequences generates a corresponding sequence of frequency distributions, which in general does not have a single limit point but a non-empty closed limit set in the space of finitely additive probabilities. This approach to randomness allows to generalize the expected utility theory in order to cover decision problems under nonstochastic random events. We derive the maxmin expected utility representation for preferences over closed sets of probability measures. The derivation is based on the axiom of preference for stochastic risk, i.e. the decision maker wishes to reduce a set of probability distributions to a single one. This complements Gilboa and Schmeidler’s (1989) consideration of the maxmin expected utility rule with objective treatment of multiple priors.


​​On the axiomatic definition of generalized maximin principle, 2016, Cybernetics and Systems Analysis 52(2): 312–318.

The paper develops the solution to the problem of axiomatic definition of the maxmin expected utility decision criteria under uncertainty. The axiomatic description of the corresponding preference relation is proposed. It is based on the specific form of the principle of guaranteed result.


Parameterization of the lottery model of nonparametric decision-making situation, 2014, Cybernetics and Systems Analysis 50(2): 234–238. (with Victor Ivanenko, Oleksiy Kuts)

The paper focuses on the parametric description of a nonparametric decision-making situation, i.e., where it is impossible to reveal the objective parameter determining the consequences of decisions. For the case of strict uncertainty, the classes of matrix schemes containing those and only those schemes that can be used to model certain nonparametric situation are described and the formula for class cardinality is proved. The cases are established where there are grounds to choose the matrix scheme with the smallest, in its class, cardinality of the set of values of the parameter.