An Incentive-Compatible and Computationally Efficient Fog Bargaining Mechanism

Kwang Mong Sim ()

Computational Economics, 2023, vol. 62, issue 4, No 19, 1883-1918

Abstract: Abstract This work contributes an (approximately) incentive-compatible and computationally efficient bargaining mechanism for pricing fog computing resources. In network settings (e.g., fog computing), it is plausible to think that self-interested and incompletely informed players (represented by software agents) will attempt to maximize their own benefits at the expense of others. Hence, it is crucial that fog bargaining mechanisms give incentives to agents for behaving in a manner consistent with the desired outcome where every agent’s benefit is maximized. Equilibrium analyses validate that the fog bargaining mechanism in this work is approximately Bayesian incentive compatible because every agent can approximately maximize its expected utility by adhering to the strategy recommended by the bargaining mechanism given that all other agents also adhere to their equilibrium strategies. That is, if every agent in the market adheres to the strategy recommended by the bargaining mechanism, then the strategy profile of the agents forms an approximate Bayesian Nash equilibrium. Given that a fog resource market has a large number of buyers and a large number of sellers, computational efficiency is also imperative since every agent needs to process a huge number of trading alternatives. Computational complexity analyses validate that 1) the procedure for carrying out the bargaining strategy has a linear time complexity, and with every passing round, the search space dwindles but the solutions become progressively better, 2) the number of rounds for each agent to complete bargaining is logarithmic in the number of its opponents, and 3) each agent has a linear message complexity.

Keywords: Algorithmic mechanism design; Bargaining mechanism; Bayesian incentive compatibility; $$\epsilon$$ ϵ -Bayesian incentive compatibility; Bayesian Nash equilibrium; Computational complexity (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10324-9

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