Cited by 7Game Theory and ApplicationsGame Theory and Voting SystemsEvolutionary Game Theory and Cooperation

Commitment and extortion

Harrenstein, Paul, Brandt, Felix, Fischer, Felix·2007·Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems

Abstract

Making commitments, e.g., through promises and threats, enables a player to exploit the strengths of his own strategic position as well as the weaknesses of that of his opponents. Which commitments a player can make with credibility depends on the circumstances. In some, a player can only commit to the performance of an action, in others, he can commit himself conditionally on the actions of the other players. Some situations even allow for commitments on commitments or for commitments to randomized actions. We explore the formal properties of these types of (conditional) commitment and their interrelationships. So as to preclude inconsistencies among conditional commitments, we assume an order in which the players make their commitments. Central to our analyses is the notion of an extortion, which we define, for a given order of the players, as a profile that contains, for each player, an optimal commitment given the commitments of the players that committed earlier. On this basis, we investigate for different commitment types whether it is advantageous to commit earlier rather than later, and how the outcomes obtained through extortions relate to backward induction and Pareto efficiency.

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Crossref · 10.1145/1329125.1329157

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  • Advanced AI promises to lower the cost and broaden the scope of coercion and extortion-whether by exposing private data through surveillance or by mounting cyber-offensive operations against rival agents-potentially weaponizing adversarial attacks, jailbreaks, and resource denial at scale.

    “Moreover, advanced AI promises to lower the cost and broaden the scope of coercion and extortion -whether by exposing private data through surveillance or by mounting cyber-offensive operations against rival agents -potentially weaponizing adversarial attacks, jailbreaks, and resource denial at scale (Ellsberg, 1968;Harrenstein et al., 2007;Zou et al., 2023;Gleave et al., 2020;Brundage et al., 202”

    From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Harrenstein et al., 2007

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Authors
Harrenstein, P., Brandt, F., & Fischer, F.
Affiliations
Ludwig-Maximilians-Universität München (DE)
Venue
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Publisher
ACM
Published
2007-05-14
Pages
1-8
ISSN
1548-8403 (ISSN-L)
OpenAlex
W1972958540
Cited by
7
References
0
Citations by year
2021: 1 · 2016: 2 · 2013: 2 · 2012: 1
FWCI
0.50
Type
conference paper
Language
en
Open access
closed
Locations
Adaptive Agents and Multi-Agents Systems (publishedVersion)
Funders
Deutsche Forschungsgemeinschaft (BR 2312/3-1)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
  • Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
Keywords
commitment, conditional commitments, credibility, extortion, backward induction, Pareto optimality, strategic interaction

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