Abstract
The realization that selfish interests need to be accounted for in the design of algorithms has produced many interesting and valuable contributions in computer science under the general umbrella of algorithmic mechanism design. Novel algorithmic properties and paradigms have been identified and studied in the literature. Our work stems from the observation that selfishness is different from rationality; agents will attempt to strategize whenever they perceive it to be convenient according to their imperfect rationality. Recent work in economics [18] has focused on a particular notion of imperfect rationality, namely absence of contingent reasoning skills, and defined obvious strategyproofness (OSP) as a way to deal with the selfishness of these agents. Essentially, this definition states that to care for the incentives of these agents, we need not only pay attention about the relationship between input and output, but also about the way the algorithm is run. However, it is not clear to date what algorithmic approaches ought to be used for OSP. In this paper, we rather surprisingly show that, for binary allocation problems, OSP is fully captured by a natural combination of two well-known and extensively studied algorithmic techniques: forward and reverse greedy. We call two-way greedy this underdeveloped algorithmic design paradigm.
Our main technical contribution establishes the connection between OSP and two-way greedy. We build upon the recently introduced cycle monotonicity technique for OSP [9]. By means of novel structural properties of cycles and queries of OSP mechanisms, we fully characterize these mechanisms in terms of extremal implementations. These are protocols that ask each agent to consistently separate one extreme of their domain at the current history from the rest. Through the natural connection with the greedy paradigm, we are able to import a host of known approximation bounds to OSP and strengthen the strategic properties of this family of algorithms. Finally, we begin exploring the full power of two-way greedy (and, in turns, OSP) in the context of set systems.
Diodato Ferraioli is supported by GNCS-INdAM and the Italian MIUR PRIN 2017 Project ALGADIMAR “Algorithms, Games, and Digital Markets”. Carmine Ventre acknowledges funding from the UKRI Trustworthy Autonomous Systems Hub (EP/V00784X/1).
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Notes
- 1.
It may appear that an alternative formalization of the interleaving between in- and out-priorities could be a query where the type is fully revealed; this would not work as there is still one type for which the outcome is undetermined.
- 2.
Note that a syntactically (but not semantically) alternative definition of forward greedy algorithms could do without \(\mathcal I\) by requiring an extra property on the priority functions (i.e., adaptively floor all the priorities of infeasible players).
- 3.
For notational simplicity, we here assume that there are not ties between the priority functions.
- 4.
The algorithm must not necessarily have a definition for the priority functions for all the combinations of type/history as some might never get explored. In this case, we set all the undefined entries to sufficiently small (tie-less, for simplicity) values.
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Ferraioli, D., Penna, P., Ventre, C. (2022). Two-Way Greedy: Algorithms for Imperfect Rationality. In: Feldman, M., Fu, H., Talgam-Cohen, I. (eds) Web and Internet Economics. WINE 2021. Lecture Notes in Computer Science(), vol 13112. Springer, Cham. https://doi.org/10.1007/978-3-030-94676-0_1
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