Group Reputations, Stereotypes, and Cooperation in a Repeated Labor Market
Reputation effects and other-regarding preferences have both been used to predict cooperative outcomes in markets with inefficient equilibria. Existing reputationbuilding models require either infinite time horizons or publicly observed identities, but cooperative outcomes have been observed in several moral hazard experiments with finite horizons and anonymous interactions. This paper introduces a full reputation equilibrium (FRE) with stereotyping (perceived type correlation) in which cooperation is predicted in early periods of a finitely repeated market with anonymous interactions. New experiments generate results in line with the FRE prediction, including final-period reversions to stage-game equilibrium and noncooperative play under unfavorable payoff parameters.
Here’s what matters: You’ve got a bad reputation. As soon as you walk through the door, your gray-haired coworkers will have their antennae up, waiting for you to make the typical millennial missteps. If you play into their low expectations, even a minor mistake may overshadow your abilities and hinder your chances for advancement. If, on the other hand, you know where their sensitivities lie, you’ll be able defy expectations, and perhaps even use those stereotypes about Gen Y to your advantage.
are they using computers in bisness to “judge” each other before they ever even know they exsist?
Trust beyond reputation: A computational trust model based on stereotypes
Models of computational trust support users in taking decisions. They are commonly used to guide users’ judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger’s actions in absence of the knowledge of such behavioral history, we often use our “instinct”- essentially stereotypes developed from our past interactions with other “similar” persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger’s profile. Since stereotypes are formed locally, recommendations stem from the trustor’s own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information.