Abstract
Computer science advocates institutional frameworks as an effective tool for modelling policies and reasoning about their interplay. In practice, the rules or policies, of which the institutional framework consists, are often specified using a formal language, which allows for the full verification and validation of the framework (e.g. the consistency of policies) and the interplay between the policies and actors (e.g. violations). However, when modelling large-scale realistic systems, with numerous decision-making entities, scalability and complexity issues arise making it possible only to verify certain portions of the problem without reducing the scale. In the social sciences, agent-based modelling is a popular tool for analysing how entities interact within a system and react to the system properties. Agent-based modelling allows the specification of complex decision-making entities and experimentation with large numbers of different parameter sets for these entities in order to explore their effects on overall system performance. In this paper we describe how to achieve the best of both worlds, namely verification of a formal specification combined with the testing of large-scale systems with numerous different actor configurations. Hence, we offer an approach that allows for reasoning about policies, policy making and their consequences on a more comprehensive level than has been possible to date. We present the institutional agent-based model methodology to combine institutional frameworks with agent-based simulations). We furthermore present J-InstAL, a prototypical implementation of this methodology using the InstAL institutional framework whose specifications can be translated into a computational model under the answer set semantics, and an agent-based simulation based on the jason tool. Using a simplified contract enforcement example, we demonstrate the functionalities of this prototype and show how it can help to assess an appropriate fine level in case of contract violations.
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Notes
The computational modelling technique corresponding to agent-based modelling is typically referred to as agent-based simulation or multi-agent simulation (Gulyás 2005).
The explanation of the terms agent and ABM in this paper follows that of Balke (2011) to a large extent.
We assume here that the institutional framework includes policies that specify the procedure and the rules of contracting.
That is, to run simulations with all possible combinations of the values of the subsets of chosen input variables across all factors. Factorial experiments enable study of the effect of each factor on the simulation output data, as well as the effects of interactions between factors, while cancelling out influences of other factors on a particular setting.
If fewer agents have violated than punishable events, all violators are fined.
The higher numbers of punishable events in our experiments (i.e. 10 and 20), that are not depicted in Fig. 5, yield the same results that followed the same trends as described here, the decrease in cheating events produced by them was however faster.)
For maximum 2 punishments effects the results are similar the single punishment effect.
An interesting line of research would be to analyse the learning or increase of awareness of agents with respect to policies if they are punished repeatedly, but this is not the subject of this paper.
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Acknowledgments
The research of Tina Balke is partially supported by funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 288147.
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Appendix
Appendix
1.1 Full case-study specification
The complete InstAL specification language description of our case study is given in the following figures. Comments describing the statements are preceeded by %. The simulation component will provide the values for the various types via the institutional monitor, i.e. name of the Agents, the values for the Rounds.
See Appendix Figs. 6, 7, 8, 9 and 10.
Declaration of the institutional components
Event generation
Initiation of fluents
Termination of fluents
Initial state
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Balke, T., De Vos, M. & Padget, J. I-ABM: combining institutional frameworks and agent-based modelling for the design of enforcement policies. Artif Intell Law 21, 371–398 (2013). https://doi.org/10.1007/s10506-013-9143-1
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DOI: https://doi.org/10.1007/s10506-013-9143-1