Commitment and extortion
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Cited by
Sorted by citations
Emergence of Social Punishment and Cooperation through Prior Commitments
Han, T. A. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10120 · Cited by 33 · see citing works
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.02077 · Cited by 2 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review