Reputation and Credit without Collateral in Africa’s Formal Banking

The analysis of reputation as a contract enforcement instrument where legal institutions, especially commercial courts, fail to enforce commercial contracts has focused on informal credit markets. The literature centres on the argument that lenders or co-borrowers in group lending can easily monitor each borrower, given the small size of an individual lender.s market. Verifiability allows the detection of opportunistic default and hence allows its punishment. This paper argues that in Africa, even formal credit markets rely on reputation. However, the modelling strategy is not based on monitoring and verifiability, given the potential for residual information asymmetry between a bank and a borrower after screening. Instead, the paper conceptualises the relationship between a bank and a borrower as an infinitely repeated game. The bank learns the type of the borrower through repeated interaction, a process by which a borrower builds his reputation as an honest partner. A defaulting dishonest borrower forfeits his access to future loans. The main result of the model is that the higher the reputation of a borrower, the lower his equilibrium payoff that is incentive compatible with debt repayment. Conversely, in the absence of any reputation, the payoff that is incentive compatible with repayment is equal to infinity meaning that credit trade is impossible without either a credible formal contract enforcement mechanism or some level of reputation.

An incentive compatible reputation mechanism for ubiquitous computing environments

International audienceThe vision of ubiquitous computing is becoming a reality thanks to the advent of portable devices and the advances in wireless networking technologies. It aims to facilitate user tasks through seamless utilization of services available in the surrounding environments. In such distributed environments featuring openness, interactions, especially service provision and consumption, between entities that are unknown or barely known to each other, are commonplace. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of ubiquitous computing. It is, however, faced by the problems of how to stimulate reputation information sharing and honest recommendation elicitation. We present in this paper an incentive compatible reputation mechanism to facilitate the trustworthiness evaluation in ubiquitous computing environments. It is based on probability theory and supports reputation evolution and propagation. Our reputation mechanism not only shows robustness against lies, but also stimulates honest and active recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can elicit the most honest (helpful) recommendations and thus suffer the least number of wrong trust decisions, as validated by simulation based evaluation

Ensuring Trust in One Time Exchanges: Solving the QoS Problem

We describe a pricing structure for the provision of IT services that ensures trust without requiring repeated interactions between service providers and users. It does so by offering a pricing structure that elicits truthful reporting of quality of service (QoS) by providers while making them profitable. This mechanism also induces truth-telling on the part of users reserving the service

Mechanism design for eliciting probabilistic estimates from multiple suppliers with unknown costs and limited precision

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly probabilistic estimate of some unknown parameter, by eliciting and fusing estimates from multiple suppliers. Each of these suppliers is capable of producing a probabilistic estimate of any precision, up to a privately known maximum, and by fusing several low precision estimates together the centre is able to obtain a single estimate with a specified minimum precision. Specifically, in the mechanism's first stage M from N agents are pre-selected by eliciting their privately known costs. In the second stage, these M agents are sequentially approached in a random order and their private maximum precision is elicited. A payment rule, based on a strictly proper scoring rule, then incentivises them to make and truthfully report an estimate of this maximum precision, which the centre fuses with others until it achieves its specified precision. We formally prove that the mechanism is incentive compatible regarding the costs, maximum precisions and estimates, and that it is individually rational. We present empirical results showing that our mechanism describes a family of possible ways to perform the pre-selection in the first stage, and formally prove that there is one that dominates all others

Mechanism design for eliciting probabilistic estimates from multiple suppliers with unknown costs and limited precision

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly probabilistic estimate of some unknown parameter, by eliciting and fusing estimates from multiple suppliers. Each of these suppliers is capable of producing a probabilistic estimate of any precision, up to a privately known maximum, and by fusing several low precision estimates together the centre is able to obtain a single estimate with a specified minimum precision. Specifically, in the mechanism's first stage M from N agents are pre-selected by eliciting their privately known costs. In the second stage, these M agents are sequentially approached in a random order and their private maximum precision is elicited. A payment rule, based on a strictly proper scoring rule, then incentivises them to make and truthfully report an estimate of this maximum precision, which the centre fuses with others until it achieves its specified precision. We formally prove that the mechanism is incentive compatible regarding the costs, maximum precisions and estimates, and that it is individually rational. We present empirical results showing that our mechanism describes a family of possible ways to perform the pre-selection in the first stage, and formally prove that there is one that dominates all others

Economics of intelligent selection of wireless access networks in a market-based framework : a game-theoretic approach

The Digital Marketplace is a market-based framework where network operators offer communications services with competition at the call level. It strives to address a tussle between the actors involved in a heterogeneous wireless access network. However, as with any market-like institution, it is vital to analyze the Digital Marketplace from the strategic perspective to ensure that all shortcomings are removed prior to implementation. In this paper, we analyze the selling mechanism proposed in the Digital Marketplace. The mechanism is based on a procurement first-price sealed-bid auction where the network operators represent the sellers/bidders, and the end-user of a wireless service is the buyer. However, this auction format is somewhat unusual as the winning bid is a composition of both the network operator’s monetary bid and their reputation rating. We create a simple economic model of the auction, and we show that it is mathematically intractable to derive the equilibrium bidding behavior when there are N network operators, and we make only generic assumptions about the structure of the bidding strategies. We then move on to consider a scenario with only two network operators, and assume that network operators use bidding strategies which are linear functions of their costs. This results in the derivation of the equilibrium bidding behavior in that scenario

Reputation in multi agent systems and the incentives to provide feedback

The emergence of the Internet leads to a vast increase in the number of interactions between parties that are completely alien to each other. In general, such transactions are likely to be subject to fraud and cheating. If such systems use computerized rational agents to negotiate and execute transactions, mechanisms that lead to favorable outcomes for all parties instead of giving rise to defective behavior are necessary to make the system work: trust and reputation mechanisms. This paper examines different incentive mechanisms helping these trust and reputation mechanisms in eliciting users to report own experiences honestly. --Trust,Reputation