We investigate law enforcement within a population of norm- governed learning agents using a probabilistic rule-based argumentation framework. We show that this formal framework can advantageously complete a traditional analysis based on expected utilities for the study of law enforcement systems when more realistic assumptions than hyper-rational agents or some behavourial phenomenon such as inertia are desired. This has signicant implications for the design of systems of retributive justice for self-organising electronic institutions with endoge- nous resources, where the cost of monitoring and enforcement of laws and norms has to be taken into consideration.
On Law Enforcement in Norm-Governed Learning Agents
RIVERET, REGIS;CONTISSA, GIUSEPPE;ROTOLO, ANTONINO;
2012
Abstract
We investigate law enforcement within a population of norm- governed learning agents using a probabilistic rule-based argumentation framework. We show that this formal framework can advantageously complete a traditional analysis based on expected utilities for the study of law enforcement systems when more realistic assumptions than hyper-rational agents or some behavourial phenomenon such as inertia are desired. This has signicant implications for the design of systems of retributive justice for self-organising electronic institutions with endoge- nous resources, where the cost of monitoring and enforcement of laws and norms has to be taken into consideration.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
