SUMMARYWe consider Internet-based master-worker task computations, such as SETI@home, where a master process sends tasks, across the Internet, to worker processes; workers execute and report back some result. However, these workers are not trustworthy, and it might be at their best interest to report incorrect results. In such master-worker computations, the behavior and the best interest of the workers might change over time. We model such computations using evolutionary dynamics, and we study the conditions under which the master can reliably obtain task results. In particular, we develop and analyze an algorithmic mechanism based on reinforcement learning to provide workers with the necessary incentives to eventually become truthful. Our analysis identifies the conditions under which truthful behavior can be ensured and bounds the expected convergence time to that behavior. The analysis is complemented with illustrative simulations.
Abstract. We consider Internet-based Master-Worker task computing systems, such as SETI@home, where a master sends tasks to potentially unreliable workers, and the workers execute and report back the result. We model such computations using evolutionary dynamics and consider three type of workers: altruistic, malicious and rational. Altruistic workers always compute and return the correct result, malicious workers always return an incorrect result, and rational (selfish) workers decide to be truthful or to cheat, based on the strategy that increases their benefit. The goal of the master is to reach eventual correctness, that is, reach a state of the computation that always receives the correct results. To this respect, we propose a mechanism that uses reinforcement learning to induce a correct behavior to rational workers; to cope with malice we employ reputation schemes. We analyze our reputation-based mechanism modeling it as a Markov chain and we give provable guarantees under which truthful behavior can be ensured. Simulation results, obtained using parameter values that are likely to occur in practice, reveal interesting trade-offs between various metrics, parameters and reputation types, affecting cost, time of convergence to a truthful behavior and tolerance to cheaters.
We design a mechanism for Fair and Efficient Distribution of Resources (FEDoR) in the presence of strategic agents. We consider a multiple-instances, Bayesian setting, where in each round the preference of an agent over the set of resources is a private information. We assume that in each of r rounds n agents are competing for k non-identical indivisible goods, (n > k). In each round the strategic agents declare how much they value receiving any of the goods in the specific round. The agent declaring the highest valuation receives the good with the highest value, the agent with the second highest valuation receives the second highest valued good, etc. Hence we assume a decision function that assigns goods to agents based on their valuations. The novelty of the mechanism is that no payment scheme is required to achieve truthfulness in a setting with rational/strategic agents. The FEDoR mechanism takes advantage of the repeated nature of the framework, and through a statistical test is able to punish the misreporting agents and be fair, truthful, and socially efficient. FEDoR is fair in the sense that, in expectation over the course of the rounds, all agents will receive the same good the same amount of times. FEDoR is an eligible candidate for applications that require fair distribution of resources over time. For example, equal share of bandwidth for nodes through the same point of access. But further on, FEDoR can be applied in less trivial settings like sponsored search, where payment is necessary and can be given in the form of a flat participation fee. FEDoR can be a good candidate in a setting like that to solve the problem of starvation of publicity slots for some advertisers that have a difficult time determining their true valuations. To this extent we perform a comparison with traditional mechanisms applied to sponsored search, presenting the advantage of FEDoR.
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