Distributed Time-Sensitive Task Selection in Mobile Crowdsensing

With the rich set of embedded sensors installed in smartphones and the large number of mobile users, we witness the emergence of many innovative commercial mobile crowdsensing applications that combine the power of mobile technology with crowdsourcing to deliver time-sensitive and location-dependent information to their customers. Motivated by these real-world applications, we consider the task selection problem for heterogeneous users with different initial locations, movement costs, movement speeds, and reputation levels. Computing the social surplus maximization task allocation turns out to be an NP-hard problem. Hence we focus on the distributed case, and propose an asynchronous and distributed task selection (ADTS) algorithm to help the users plan their task selections on their own. We prove the convergence of the algorithm, and further characterize the computation time for users' updates in the algorithm. Simulation results suggest that the ADTS scheme achieves the highest Jain's fairness index and coverage comparing with several benchmark algorithms, while yielding similar user payoff to a greedy centralized benchmark. Finally, we illustrate how mobile users coordinate under the ADTS scheme based on some practical movement time data derived from Google Maps

Learning to Reach Agreement in a Continuous Ultimatum Game

It is well-known that acting in an individually rational manner, according to the principles of classical game theory, may lead to sub-optimal solutions in a class of problems named social dilemmas. In contrast, humans generally do not have much difficulty with social dilemmas, as they are able to balance personal benefit and group benefit. As agents in multi-agent systems are regularly confronted with social dilemmas, for instance in tasks such as resource allocation, these agents may benefit from the inclusion of mechanisms thought to facilitate human fairness. Although many of such mechanisms have already been implemented in a multi-agent systems context, their application is usually limited to rather abstract social dilemmas with a discrete set of available strategies (usually two). Given that many real-world examples of social dilemmas are actually continuous in nature, we extend this previous work to more general dilemmas, in which agents operate in a continuous strategy space. The social dilemma under study here is the well-known Ultimatum Game, in which an optimal solution is achieved if agents agree on a common strategy. We investigate whether a scale-free interaction network facilitates agents to reach agreement, especially in the presence of fixed-strategy agents that represent a desired (e.g. human) outcome. Moreover, we study the influence of rewiring in the interaction network. The agents are equipped with continuous-action learning automata and play a large number of random pairwise games in order to establish a common strategy. From our experiments, we may conclude that results obtained in discrete-strategy games can be generalized to continuous-strategy games to a certain extent: a scale-free interaction network structure allows agents to achieve agreement on a common strategy, and rewiring in the interaction network greatly enhances the agents ability to reach agreement. However, it also becomes clear that some alternative mechanisms, such as reputation and volunteering, have many subtleties involved and do not have convincing beneficial effects in the continuous case

Collusion in Peer-to-Peer Systems

Peer-to-peer systems have reached a widespread use, ranging from academic and industrial applications to home entertainment. The key advantage of this paradigm lies in its scalability and flexibility, consequences of the participants sharing their resources for the common welfare. Security in such systems is a desirable goal. For example, when mission-critical operations or bank transactions are involved, their effectiveness strongly depends on the perception that users have about the system dependability and trustworthiness. A major threat to the security of these systems is the phenomenon of collusion. Peers can be selfish colluders, when they try to fool the system to gain unfair advantages over other peers, or malicious, when their purpose is to subvert the system or disturb other users. The problem, however, has received so far only a marginal attention by the research community. While several solutions exist to counter attacks in peer-to-peer systems, very few of them are meant to directly counter colluders and their attacks. Reputation, micro-payments, and concepts of game theory are currently used as the main means to obtain fairness in the usage of the resources. Our goal is to provide an overview of the topic by examining the key issues involved. We measure the relevance of the problem in the current literature and the effectiveness of existing philosophies against it, to suggest fruitful directions in the further development of the field

Use and Abuse of Authority

Employment contracts give a principal the authority to decide flexibly which task his agent should execute. However, there is a tradeoff, first pointed out by Simon (1951), between flexibility and employer moral hazard. An employment contract allows the principal to adjust the task quickly to the realization of the state of the world, but he may also abuse this flexibility to exploit the agent. We capture this tradeoff in an experimental design and show that principals exhibit a strong preference for the employment contract. However, selfish principals exploit agents in one-shot interactions, inducing them to resist entering into employment contracts. This resistance to employment contracts vanishes if fairness preferences in combination with reputation opportunities keep principals from abusing their power, leading to the widespread, endogenous formation of efficient long-run employment relations. Our results inform the theory of the firm by showing how behavioral forces shape an important transaction cost of integration – the abuse of authority – and by providing an empirical basis for assessing differences between the Marxian and the Coasian view of the firm, as well as Alchian and Demsetz’s (1972) critique of the Coasian approach

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Does Generosity Generate Generosity? An Experimental Study of Reputation Effects in a Dictator Game

This paper explores how information about paired subject's previous action affects one's own behavior in a dictator game. The first experiment puts dictators in two environments where they can either give money to the paired player or take money away from them: one where the recipient is a stranger and the other where the dictator has information on the recipient's reputation. Contrary to anecdotal evidence, the statistical tests show that the dictator's behavior toward a stranger is not statistically significantly different from their behavior toward an individual with an established reputation. The findings arise because a high proportion of dictators acted purely in their own self interest in both treatments. In the second experiment the dictators' choices were restricted to only generous actions. In such environment the dictators sent more money on average to recipients with a reputation for being generous than to recipients without a reputation.Experimental economics; dictator game; indirect reciprocity; reputation; generosity

The Economics of Fairness, Reciprocity and Altruism – Experimental Evidence and New Theories

This paper surveys recent experimental and field evidence on the impact of concerns for fairness, reciprocity and altruism on economic decision making. It also reviews some new theoretical attempts to model the observed behavior.Behavioural Economics; Other-regarding Preferences; Fairness; Reciprocity; Altruism; Experiments; Incentives; Contracts; Competition