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Register a new userEgoistic Incentives Based on Zero-Determinant Alliances for Large-Scale Systems
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Shengling Wang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Social dilemmas exist in various fields and give rise to the so-called free-riding problem, leading to collective fiascos. The difficulty of tracking individual behaviors makes egoistic incentives in large-scale systems a challenging task. However, the state-of-the-art mechanisms are either individual-based or state-dependent, resulting in low efficiency in large-scale networks. In this paper, we propose an egoistic incentive mechanism from a connected (network) perspective rather than an isolated (individual) perspective by taking advantage of the social nature of people. We make use of a zero-determinant (ZD) strategy for rewarding cooperation and sanctioning defection. After proving cooperation is the dominant strategy for ZD players, we optimize their deployment to facilitate cooperation over the whole system. To further speed up cooperation, we derive a ZD alliance strategy for sequential multiple-player repeated games to empower ZD players with higher controllable leverage, which undoubtedly enriches the theoretical system of ZD strategies and broadens their application domain. Our approach is stateless and stable, which contributes to its scalability. Extensive simulations based on a real world trace data as well as synthetic data demonstrate the effectiveness of our proposed egoistic incentive approach under different networking scenarios.
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A formula is presented for designing zero-determinant(ZD) strategies of general finite games, which have $n$ players and players can have different numbers of strategies. To this end, using semi-tensor product (STP) of matrices, the profile evolutionary equation for repeated finite games is obtained. Starting from it, the ZD strategies are developed for general finite games, based on the same technique proposed by Press and Dyson cite{pre12}. A formula is obtain to design ZD strategies for any player directly, ignoring the original ZD design process. Necessary and sufficient condition is obtained to ensure the effectiveness of the designed ZD strategies. As a consequence, it is also clear that player $i$ is able to unilaterally design $|S_i|-1$ dominating linear relations about the expected payoffs of all players. Finally, the fictitious opponent player is proposed for networked evolutionary networks (NEGs). Using it, the ZD-strategies are applied to NEGs. It is surprising that an individual in a network may use ZD strategies to conflict the whole rest network.
Recently, Press and Dyson have proposed a new class of probabilistic and conditional strategies for the two-player iterated Prisoners Dilemma, so-called zero-determinant strategies. A player adopting zero-determinant strategies is able to pin the expected payoff of the opponents or to enforce a linear relationship between his own payoff and the opponents payoff, in a unilateral way. This paper considers zero-determinant strategies in the iterated public goods game, a representative multi-player evolutionary game where in each round each player will choose whether or not put his tokens into a public pot, and the tokens in this pot are multiplied by a factor larger than one and then evenly divided among all players. The analytical and numerical results exhibit a similar yet different scenario to the case of two-player games: (i) with small number of players or a small multiplication factor, a player is able to unilaterally pin the expected total payoff of all other players; (ii) a player is able to set the ratio between his payoff and the total payoff of all other players, but this ratio is limited by an upper bound if the multiplication factor exceeds a threshold that depends on the number of players.
Repeated game theory has been one of the most prevailing tools for understanding the long-run relationships, which are footstones in building human society. Recent works have revealed a new set of zero-determinant (ZD) strategies, which is an important advance in repeated games. A ZD strategy player can exert a unilaterally control on two players payoffs. In particular he can deterministically set the opponents payoff, or enforce an unfair linear relationship between the players payoffs, thereby always seizing an advantageous share of payoffs. One of the limitations of the original ZD strategy, however, is that it does not capture the notion of robustness when the game is subjected to stochastic errors. In this paper, we propose a general model of ZD strategies for noisy repeated games, and find that ZD strategies have high robustness against errors. We further derive the pinning strategy under noise, by which the ZD strategy player coercively set the opponents expected payoff to his desired level, although his payoff control ability declines with the increase of noise strength. Due to the uncertainty caused by noise, the ZD strategy player cannot secure his payoff to be higher than the opponents, which implies strong extortions do not exist even under low noise. While we show that the ZD strategy player can still establish a novel kind of extortions, named weak extortions, where any increase of his own payoff always exceeds that of the opponents by a fixed percentage, and the conditions under which the weak extortions can be realized are more stringent as the noise becomes stronger.
Proper incentive mechanisms are critical for mobile crowdsensing systems to motivate people to actively and persistently participate. This article provides an exposition of design principles of six incentive mechanisms, drawing special attention to the sustainability issue. We cover three primary classes of incentive mechanisms: auctions, lotteries, and trust and reputation systems, as well as three other frameworks of promising potential: bargaining games, contract theory, and market-driven mechanisms.
We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones [Joseph et al]. We investigate whether it is possible to design inexpensive {subsidy} or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost $o(T)$ (for the classic setting with $k$ arms, $tilde{O}(sqrt{k^3T})$, and for the $d$-dimensional linear contextual setting $tilde{O}(dsqrt{k^3 T})$). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the $k$ groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes.
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