Many natural processes rely on optimizing the success ratio of an underlying search process. We investigate how fluxes of information between individuals and their environment modify the statistical properties of human search strategies. In this talk I propose the use of an online game to study this problem. Players are requested to find a hidden target whose location is hinted by a surrounding neighborhood. Searches are optimal for intermediate neighborhood sizes. Larger neighborhoods are easier to locate but make the final detection of the target inside them harder. Smaller neighborhoods, however, need on average more steps to be found but make the target within them easier to locate. Although the neighborhood size that minimizes average search times depends on the geometry of the neighborhood, we develop a theoretical framework to predict this value in a general setup. Furthermore, a priori access to information about the landscape turns search strategies into self-adaptive processes in which the trajectory on the board evolves to show a well-defined characteristic jumping length. A family of random-walk models is developed to investigate the non-Markovian nature of the process.
Reference: R. Martinez-Garcia, J.M. Calabrese & C. Lopez, arXiv:1606.06850.
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