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Greater Flexibility in Mechanism Design Through Altruism

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Algorithmic Game Theory (SAGT 2022)

Abstract

We study the problem of designing truthful mechanisms for players that are (partially) altruistic. Our approach is to extend the standard utility model by encoding other-regarding preferences of the players into the utility functions. By doing so we leave the original domain where VCG mechanisms can be applied directly.

We derive a characterization of the class of truthful mechanisms under the new model, crucially exploiting the specific form of the other-regarding preferences. We also derive sufficient conditions for truthfulness, which we then exploit to derive mechanisms for two specific models of altruism and with respect to two natural social welfare objectives. As it turns out, altruistic dispositions lead to the positive effect that the designer needs to extract smaller payments from the players to ensure truthfulness. Further, we investigate the effect of redistribution mechanisms that can redistribute the payments among the players. Also here, it turns out that altruism has a positive effect in the sense that the payments needed to guarantee truthfulness can be further reduced.

Finally, we illustrate our theoretical results by applying them to well-studied mechanism design problems such as the public project problem and the multi-unit auction problem. Among other results, we show that the problem of funding a public project can be resolved by our mechanism even for moderate altruistic dispositions, while this is impossible in the standard utility setting.

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Notes

  1. 1.

    Intuitively, the function \(g_i\) of player i can be viewed as being dependent on the reported valuation functions \(\vec {b}_{-i}\) of the other players and the payment functions \(\vec {p}\). Formally, however, \(g_i\) only depends on the respective values of these functions under the outcome \((f(\vec {b}),\vec {p}(\vec {b}))\) determined by the mechanism \(M = (f,\vec {p})\) when run on \(\vec {b}\).

  2. 2.

    Note that although our focus here is on altruism levels in the range [0, 1], many results given below can be extended in a straightforward way to other cases such as spiteful players (\(\alpha _i < 0\)) or players that care about others more than about themselves (\(\alpha _i > 1\)).

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Acknowledgements

Part of this work was done when Sjir Hoeijmakers was at CWI.

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Correspondence to Ruben Brokkelkamp .

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Brokkelkamp, R., Hoeijmakers, S., Schäfer, G. (2022). Greater Flexibility in Mechanism Design Through Altruism. In: Kanellopoulos, P., Kyropoulou, M., Voudouris, A. (eds) Algorithmic Game Theory. SAGT 2022. Lecture Notes in Computer Science, vol 13584. Springer, Cham. https://doi.org/10.1007/978-3-031-15714-1_3

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  • DOI: https://doi.org/10.1007/978-3-031-15714-1_3

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