We modify the principal-agent model with moral hazard by assuming that the agent is expectation-based loss averse according to Köszegi and Rabin (2006, 2007). The optimal contract is a binary payment scheme even for a rich performance measure, where standard preferences predict a fully...

We modify the principal-agent model with moral hazard by assuming that the agent is expectation-based loss averse according to Köszegi and Rabin (2006, 2007). The optimal contract is a binary payment scheme even for a rich performance measure, where standard preferences predict a fully...

This paper provides a comprehensive analysis regarding strategic interaction under expectation-based loss-aversion. First, we develop a coherent framework for the analysis by extending the equilibrium concepts of Koszegi and Rabin (2006, 2007) to strategic interaction and demonstrate how to...

Extending the equilibrium concepts of Kőszegi and Rabin (2006, 2007), this paper analyzes the strategic interaction of expectation-based loss-averse players. For loss-averse players with choice-acclimating expectations the utility from playing a mixed strategy is not linear but convex in the...

Several empirical findings have challenged the traditional trade-off between risk and incentives. By combining risk aversion and limited liability in a standard principal-agent model the empirical puzzle on the positive relationship between risk and incentives can be explained.

We analyze the optimal choice of risk in a two-stage tournament game between two players that have different concave utility functions. At the first stage, both players simultaneously choose risk. At the second stage, both observe overall risk and simultaneously decide on effort or investment....

We analyze the optimal choice of risk in a two-stage tournament game between two players that have different concave utility functions. At the first stage, both players simultaneously choose risk. At the second stage, both observe overall risk and simultaneously decide on effort or investment....