Decision making under uncertainty based on data: awareness, learning and interpersonal consistency 

This project is funded by the programm FRAL, a joint programm of the French National Research Agency (ANR) and the German Research Foundation (DFG)

Scientific Coordinators

Jürgen Eichberger, Heidelberg University, Alfred-Weber Institut

Ani Guerdjikova, University of Grenoble-Alpes, GAEL

Summary of the Project:

The standard model of decision making under uncertainty relies on an exogenously given, objective, and observable state-space, which is common to all agents. Fundamental economic concepts such as no-arbitrage and dominance are based on the assumption that the state-space is common knowledge. Data are used only to inform probabilities over states. But how do economic agents arrive at a state space? And how is common knowledge of the underlying uncertainty model achieved?

In this project, we propose a model in which information about possible contingencies, and about their probabilities is obtained from available data as in the case-based decision theory. Data are intrinsically incomplete and provide only partial information about the underlying state-space and the relevant probabilities. The evaluation of actions thus has to combine the objective information with subjective evaluation of uncertainty: ambiguity (uncertain probabilities over outcomes), and unawareness (uncertainty about the relevant states).

The model allows for different types of learning: Bayesian updating; learning about new states; refining the state space; and, learning about counterfactuals. Such learning leads to a revision of the subjective model of uncertainty; may lead to updating of the perception of uncertainty; and allows for adjustments of the attitudes towards uncertainty. An important question concerns the acquisition and evaluation of new information. We propose a concept for determining the value of information and relate it to the decision maker’s subjective attitude towards ambiguity and unawareness.

Since different subjective features give rise to distinct models of uncertainty, individuals with access to identical data  may differ both in terms of subjective predictions and implied behavior. Such heterogeneity has observable implications for economic behavior, allocations in markets, strategic interactions and social policy that go beyond the statistical content of data.

The proposed model is used to study relevant economic applications. First, we ask whether a society consisting of individuals who have access to the same publicly available data will agree on the model of underlying uncertainty of the economy and in particular, on the relevant state space and the corresponding probability distribution. Second, we model strategic decisions in which data about past choices in similar interactive situations co-determine equilibrium beliefs. We study the implication of data-based strategic decisions for long-term behaviour and the emergence of social institutions. Third, we show that our model provides a natural decision-theoretical counterpart to the purely objective models used in statistical learning and artificial intelligence. We use this to explore its implications in the classical problem of classification. Finally, we apply our model to analyze behavior, equilibrium allocations and prices in financial markets. The goal is to to provide explanations for stylized-facts and understand the long-run implications of data-based decisions with respect to heterogeneity of agent-specific characteristics and beliefs.

Workshop on Unforeseen Contingencies: Representations and Applications, June 5-6, 2025 UGA, GAEL

Discussion Papers:

Eichberger, J. , Grant, S., Kelsey, D. (2025).  "Ambiguity in Multi-Stage Games: Uncertainty about the Opponent’s Strategic Behavior"

Eichberger, J. , Guerdjikova, A.(2024). "Data, Cases and States"

Guerdjikova, A., Piermont, E., Quiggin, J.  (2025). "Do You Know What I Mean? A Syntactic Representation for Differential Bounded Awareness"

Work in Progress:

Eichberger, J., Pasichnichenko, I. "Value of Partial Information"

Eichberger, J. Guerdjikova, A. "Data-Based Decision Making Under Uncertainty"

Guerdjikova, A., Piermont, E., Quiggin, J. "How Do You Know What I Mean?"