Decision Theory

Decision theory seeks to offer formal representations of individual decision-making processes. One important purpose of decision theory is to develop mathematical models that account for individual decision-making. In axiomatic decision theory, in particular, instead of suggesting models directly, researchers posit testable assumptions (i.e., axioms) about behavior and find ways to represent them in mathematically convenient terms. In this way, decision theory is closely related to experimental research: axioms are tested using data obtained in controlled laboratory experiments. If some axioms are violated frequently, it highlights the necessity of modifying the model to replace the violated axiom. We then test the new axiom again by using data from carefully conducted laboratory experiments and judge how well this new axiom is able to account for the choice behavior observed in the experiments. This close relationship between theory and experiments is important for the development of new and better models of individual decision-making. 

The following are a sampling of publications in this field by scholars affiliated with CTESS:

  • "Deterrence Effects of Enforcement Schemes: an Experimental Study" (2021) by Marina Agranov and Anastasia Buyalskaya, Management Science
  • "Testable Implications of Models of Intertemporal Choice: Exponential Discounting and Its Generalizations" (2021) by Federico Echenique, Taisuke Imai, and Kota Saito, American Economic Journal: Microeconomics
  • "Model and Predictive Uncertainty: A Foundation for Smooth Ambiguity Preferences" (2021) by Tommaso Denti and Luciano Pomatto, Econometrica
  • "On Path Independent Stochastic Choice" (2018), David Ahn, Federico Echenique, and Kota Saito, Theoretical Economics
  • "Stochastic Choice and Preferences for Randomization" (2017), Marina Agranov and Pietro Ortoleva, Journal of Political Economy