Abstract
Achieving social order in societies of self-interested autonomous agents is a difficult problem due to lack of trust in the actions of others and the temptation to seek rewards at the expense of others. In human society, social norms play a strong role in fostering cooperative behaviour—as long as the value of cooperation and the cost of defection are understood by a large proportion of society. Prior work has shown the importance of both norms and metanorms (requiring punishment of defection) to produce and maintain norm-compliant behaviour in a society, e.g. as in Axelrod’s approach of learning of individual behavioural characteristics of boldness and vengefulness. However, much of this work (including Axelrod’s) uses simplified simulation scenarios in which norms are implicit in the code or are represented as simple bit strings, which limits the practical application of these methods for agents that interact across a range of real-world scenarios with complex norms. This work presents a generalisation of Axelrod’s approach in which norms are explicitly represented and agents can choose their actions after performing what-if reasoning using a version of the event calculus that tracks the creation, fulfilment and violation of expectations. This approach allows agents to continually learn and apply their boldness and vengefulness parameters across multiple scenarios with differing norms. The approach is illustrated using Axelrod’s scenario as well as a social dilemma from the behavioural game theory literature.
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Notes
- 1.
Axelrod used five runs of a hundred generations to simulate the norms and metanorms games. However, we follow the recommendation of [7] and use 100 runs.
- 2.
Axelrod does not state how he maintains a fixed population size after applying these reproduction rules. We follow the approach of [7] involving random sampling when the new population is too large, and random replication when the population is too small.
- 3.
There is also an extended version of the \( exp \) fluent with these four arguments—the version used in this paper has only the residual expectation as its argument.
- 4.
- 5.
At present we assume there are no more than two relevant actions.
- 6.
Populations with similar average levels of boldness and vengefulness are grouped together to create each vector. The end point of each arrow shows the average levels of these features one generation later [3].
- 7.
We do not include a third-order norm for the plain-plateau scenario as this was not part of Klein’s model [10].
- 8.
Run 1 of Experiment 2 ended with only one agent with a non-zero boldness value: 4/7.
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Acknowledgements
This work was supported by the Marsden Fund Council from New Zealand Government funding, managed by Royal Society Te Apārangi.
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Sengupta, A., Cranefield, S., Pitt, J. (2023). Generalising Axelrod’s Metanorms Game Through the Use of Explicit Domain-Specific Norms. In: Fornara, N., Cheriyan, J., Mertzani, A. (eds) Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XVI. COINE 2023. Lecture Notes in Computer Science(), vol 14002. Springer, Cham. https://doi.org/10.1007/978-3-031-49133-7_2
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