Learning to respect property by refashioning theft into trade
通过计算机模拟和人类实验，研究在无成本盗窃的经济中，互惠和爬山启发式如何促使财产尊重作为社会惯例自发出现，使交换取代盗窃。
Abstract Agent-based simulations and human-subject experiments explore the emergence of respect for property in a specialization and exchange economy with costless theft. Software agents, driven by reciprocity and hill-climbing heuristics and parameterized to replicate humans when property is exogenously protected, are employed to predict human behavior when property can be freely appropriated. Agents do not predict human behavior in a new set of experiments because subjects innovate, constructing a property convention of “mutual taking” in 5 out of the 6 experimental sessions that allows exchange to crowd out theft. When the same convention is made available to agents, they adopt it and again replicate human behavior. Property emerges as a social convention that exploits the capacity for reciprocity to sustain trade.