 In the spirit of rational choice theory, much of this work was inspired by analytical work in economics on infinite populations by Venkatesh Bala and Sanjeev Goyal (1998), computationally implemented for small populations in a finite context and with an eye to philosophical implications by Kevin Zollman (2007, 2010a, 2010b). In Zollman’s model, Bayesian agents choose between a current method \(\phi_1\) and what is set as a better method \(\phi_2,\) starting with random beliefs and allowing agents to pursue the investigatory action with the highest subjective utility. Agents update their beliefs based on the results of their own testing results—drawn from a distribution for that action—together with results from the other agents to which they are communicatively connected. A community is taken to have successfully learned when all agents converge on the better \(\phi_2.\)