This paper discusses the strategic role of mismatching, where players voluntarily form inefficient teams or forego the formation of efficient teams, respectively. Strategic mismatching can be rational when players realize a competitive advantage (e.g. harming other competitors). In addition, the...

This paper discusses the strategic role of mismatching, where players voluntarily form inefficient teams or forego the formation of efficient teams, respectively. Strategic mismatching can be rational when players realize a competitive advantage (e.g. harming other competitors). In addition, the...

This paper extends the standard principal-agent model with moral hazard to allow for agents having reference- dependent preferences according to Köszegi and Rabin (2006, 2007). The main finding is that loss aversion leads to fairly simple contracts. In particular, when shifting the focus from...

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 extends the standard principal-agent model with moral hazard to allow for agents having reference-dependent preferences according to Köszegi and Rabin (2006, 2007). The main finding is that loss aversion leads to fairly simple contracts. In particular, when shifting the focus from...

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...