Learning by doing vs. learning from others in a principal-agent model

Abstract

a b s t r a c tWe introduce learning in a principal-agent model of output sharing under moral hazard. We use social evolutionary learning to represent social learning and reinforcement, experience-weighted attraction (EWA) and individual evolutionary learning (IEL) to represent individual learning. Learning in the principal-agent model is difficult due to: the stochastic environment; the discontinuity in payoffs at the optimal contract; and the incorrect evaluation of foregone payoffs for IEL and EWA. Social learning is much more successful in adapting to the optimal contract than standard individual learning algorithms. A modified IEL using realized payoffs evaluation performs better but still falls short of social learning.

12. Social Learning in a Model of Adverse Selection

“…Finally, as we show in Arifovic and Karaivanov (2009), given that the maximum profits are achieved by contracts located right on the constraints - that is, on the 'cliffs' of the two elevated 'ledges' - profits drop discontinuously for a small mutation to the right, which again creates problems for the learning process.…”

Social network learning: Uncertainty, heterogeneity, and the application in principal–agent relationships

“…Adopting a similar approach, [20] investigates the efficiencies of social learning and self-enhancing algorithms based on a principal-agent model, but they do not consider the impact of network topology and heterogeneity. Through the construction of commonly encountered real-world social network structures, we elucidate how different network structures affect dual-layer networks.…”

“…The objective (20) is to find the s * that maximizes the principal's profit. Equation ( 21) specifies the incentive compatibility constraint (ICC), indicating that any agent i will make the optimal effort e * i,t to maximize the net utility on day t. Equation ( 22) specifies the participation constraint (PC).…”

Interaction between vehicles and pedestrians at uncontrolled mid-block crosswalks

“…A huge variety of microscopic update rules has been defined and applied in game theory (Szabo and Fath, 2007). These update rules can be divided into two categories of learning paradigms: one is the social learning paradigm, in which players update their strategies based on imitation of strategies of those players who have performed better in the past, and the other is the individual learning paradigm, which is based on an individual's learning and updating of strategies based only on his/her own experience (Arifovic and Karaivanov, 2010). In this study, we consider mainly social learning paradigms in order to describe the player's learning process, which may help us better understand the herd mentality (Jin et al, 2013) and capture the phenomenon of disagreement among a pedestrian crossing group, which previous works failed to reflect.…”