Showing papers by "Alexander Peysakhovich published in 2013"

How to Commit (If You Must): Commitment Contracts and the Dual-Self Model

Abstract: This paper studies how dual-self (Fudenberg and Levine (2006)) decision-makers can use commitment technologies to combat temptation and implement long-run optimal actions. I consider three types of commitment technologies: carrot contracts (rewards for 'good' behavior financed by borrowing from future consumption), stick contracts (self imposed fines for 'bad' behavior) and binding commitment. I compare the welfare implications of these contracts and show that dual-self decision-makers strictly prefer to use carrots instead of either sticks or binding commitments. This is for several reasons: sticks are highly vulnerable to trembles (while carrots are not), sticks and bind- ing commitments create a temptation to cancel them (while carrots do not), and finally carrots allow easy tradeoffs between commitment and flexibility (while sticks and binding commitments do not).

Recency, Records and Recaps: The effect of feedback on behavior in a simple decision problem*

Abstract: Suboptimal behavior can persist in simple stochastic decision problems This has motivated the development of solution concepts such as cursed equilibrium (Eyster & Rabin 2005) and behavioral equilibrium (Esponda 2008) We experimentally study a simple adverse selection (or “lemons”) problem and find that learning models that heavily discount past information (ie display recency bias) explain patterns of behavior better than Nash, cursed or behavioral equilibrium Providing counterfactual information or a record of past outcomes does little to aid convergence to optimal strategies, but providing sample averages (“recaps”) gets individuals most of the way to optimality Thus recency effects are not solely due to limited memory but stem from some other form of cognitive constraints

Recency, Records and Recaps: Learning and Non-Equilibrium Behavior in a Simple Decision Problem*

Abstract: Nash equilibrium takes optimization as a primitive, but suboptimal behavior can persist in simple stochastic decision problems. This has motivated the development of other equilibrium concepts such as cursed equilibrium and behavioral equilibrium. We experimentally study a simple adverse selection (or “lemons”) problem and find that learning models that heavily discount past information (i.e. display recency bias) explain patterns of behavior better than Nash, cursed or behavioral equilibrium. Providing counterfactual information or a record of past outcomes does little to aid convergence to optimal strategies, but providing sample averages (“recaps”) gets individuals most of the way to optimality. Thus recency effects are not solely due to limited memory but stem from some other form of cognitive constraints. Our results show the importance of going beyond static optimization and incorporating features of human learning into economic models.