Perceptions and Truth: A Mechanism Design Approach to Crowd-Sourcing Reputation

Abstract

We consider a distributed multiuser system where individual entities possess observations or perceptions of one another, while the truth is only known to themselves and they might have an interest in withholding or distorting the truth. We ask the question whether it is possible for the system as a whole to arrive at the correct perceptions or assessment of all users, referred to as their reputation, by encouraging or incentivizing the users to participate in a collective effort without violating private information and self-interest. In this paper, we investigate this problem using a mechanism design theoretic approach. We introduce a number of utility models representing users' strategic behavior, each consisting of one or both of a truth element and an image element, reflecting the user's desire to obtain an accurate view of others and an inflated image of itself. For each model, we either design a mechanism that achieves the optimal performance (solution to the corresponding centralized problem), or present individually rational suboptimal solutions. In the latter case, we demonstrate that even when the centralized solution is not achievable, by using a simple punish-reward mechanism, not only does a user have the incentive to participate and provide information, but also that this information can improve the system performance.

On social machines for algorithmic regulation

“…it sets up goals before choosing the rules of the game. In this way, it is possible to solve "a centralized problem in an informationally decentralized system" (Naghizadeh and Liu, 2016).…”

A Reputation-Based Contract for Repeated Crowdsensing With Costly Verification

“…It is worth mentioning that using reputation for mitigating information asymmetry, particularly in repeated games, is a popular strategy in the literature, see e.g. [14]- [18]. In [14], the authors address the problem of mitigating pure adverse selection by means of reputation indices in a static setting.…”

“…[14]- [18]. In [14], the authors address the problem of mitigating pure adverse selection by means of reputation indices in a static setting. The authors of [15] present a comprehensive study of the use of reputation for mitigating adverse selection in repeated games, i.e., learning the hidden types of players through repeated interactions.…”

R-bUCRP: A Novel Reputation-Based Uneven Clustering Routing Protocol for Cognitive Wireless Sensor Networks

“…The higher the reputation value of the cluster head nodes is, the more reliable the nodes are and the higher the quality of service provided by the nodes in the cluster is. Naghizadeh and Liu [26] describe the reputation problem in a distributed multiuser system by using a mechanism design theoretic approach. They introduce a number of utility models representing users' strategic behavior and design a mechanism that achieves the optimal performance.…”