Social search has stably evolved across various species and is often used by humans to search for resources (such as food, information, social partners). In turn, these resources frequently come distributed in patches or clusters. In the current work, we use an ecologically inspired agent-based model to investigate whether social search and clustering are stable outcomes of the dynamical mutual interactions between the two. While previous research has studied unidirectional influences of social search on resource clustering and vice versa, the current work investigates the consequential patterns emerging from their two-way interactions over time. In our model, consumers evolved search strategies (ranging from competitive to social) as adaptations to their environmental resource structures, and resources varied in distributions (ranging from random to clustered) that were shaped by agents' consumption patterns. Across four experiments, we systematically analyzed the patterns of influence that search strategies and environment structure have on each other to identify stable attractor states of both. In Experiment 1, we fixed resource clustering at various levels and observed its influence on social search, and in Experiment 2, we observed the influence of social search on resource distribution. In both these experiments we found that increasing levels of one variable produced increases in the other; however, at very high levels of the manipulated variable, the dependent variable tended to fall. Finally in Experiments 3 and 4, we studied the dynamics that arose when resource clustering and social search could both change and mutually influence each other, finding that low levels of social search and clustering were stable attractor states. Our simple 2D model yielded results that qualitatively resemble those across a wide range of search domains (from physical search for food to abstract search for information), highlighting some stable outcomes of mutually interacting consumer/resource systems.

There are a number of mobile shopping aids and recommender systems available, but none can be easily used for a weekly shop at a local supermarket. We present a minimal, mobile and fully functional lambent display that clips onto any shopping trolley handle, intended to nudge people when choosing what to buy. It provides salient information about the food miles for various scanned food items represented by varying lengths of lit LEDs on the handle and a changing emoticon comparing the average miles of all the products in the trolley against a social norm. When evaluated in situ, the lambent handle display nudged people to choose products with fewer food miles than the items they selected using their ordinary shopping strategies. People also felt guilty when the average mileage of the contents of their entire shopping trolley was above the social norm. The findings are discussed in terms of how to provide different kinds of product information that people care about, using simple lambent displays.

Mainstream video games are increasingly benefiting from more sophisticated adversarial artificial intelligences. The quality of these synthetic opponents is becoming a significant competitive advantage that was once exclusively reserved for graphics. This new generation of synthetic opponent relies on dynamic planning systems such as STRIPS to realize realistic and challenging adversarial behavior. Such systems have been embraced by game developers as they provide for transparent representation of agent state and behavior, have low CPU utilization and are available in toolkit form. Concurrently, the dramatic proliferation of parallel computational units in modern hardware architectures is also facilitating the use of connectionist models of artificial intelligence in gaming. However, significant barriers such as neural network training set generation and turnaround time for neural network development have inhibited widespread adoption of such techniques. To overcome these barriers, we present an infrastructure that automates neural network development through the use of a genetic algorithm to evolve the behavioral training set of an adversarial artificial intelligence. The infrastructure uses an existing game, Wolfenstein 3D, as a simulation environment. We compare the effectiveness of the neural network generated by this system against a manually constructed neural network and the original game AI. All three models are pitted against human players

Simple lexicographic decision heuristics that consider cues one at a time in a particular order and stop searching for cues as soon as a decision can be made have been shown to be both accurate and frugal in their use of information. But much of the simplicity and success of these heuristics comes from using an appropriate cue order. For instance, the Take The Best heuristic uses validity order for cues, which requires considerable computation, potentially undermining the computational advantages of the simple decision mechanism. But many cue orders can achieve good decision performance, and studies of sequential search for data records have proposed a number of simple ordering rules that may be of use in constructing appropriate decision cue orders as well. Here we consider a range of simple cue ordering mechanisms, including tallying, swapping, and move-to-front rules, and show that they can find cue orders that lead to reasonable accuracy and considerable frugality when used with lexicographic decision heuristics.

We present a model of human mate choice that shows how realistic population-level patterns of assortative mating can self-organize and emerge from the behavior of individuals using simple mate search rules. In particular, we model plausible psychological mechanisms for mate search and choice in a realistic social ecology. Through individual interactions, patterns emerge that match those observed in typical human societies, particularly with regard to correlated quality levels within couples, distributions of the ages at which couples mate, and effects of skewed sex ratios on these mating age distributions.

January 01 2002 Artificial Life Models for Musical Applications: Workshop Report Eleonora Bilotta, Eleonora Bilotta Centro Interdipartimentale della Comunicazione, Università della Calabria Cosenza, Italy, [email protected] Search for other works by this author on: This Site Google Scholar Eduardo Reck Miranda, Eduardo Reck Miranda SONY Computer Science Laboratory, Paris, France, [email protected] Search for other works by this author on: This Site Google Scholar Pietro Pantano, Pietro Pantano Centro Interdipartimentale della Comunicazione, Università della Calabria, Cosenza, Italy, [email protected] Search for other works by this author on: This Site Google Scholar Peter M. Todd Peter M. Todd Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany [email protected] Search for other works by this author on: This Site Google Scholar Author and Article Information Eleonora Bilotta Centro Interdipartimentale della Comunicazione, Università della Calabria Cosenza, Italy, [email protected] Eduardo Reck Miranda SONY Computer Science Laboratory, Paris, France, [email protected] Pietro Pantano Centro Interdipartimentale della Comunicazione, Università della Calabria, Cosenza, Italy, [email protected] Peter M. Todd Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany [email protected] Online Issn: 1530-9185 Print Issn: 1064-5462 © 2002 Massachusetts Institute of Technology2002 Artificial Life (2002) 8 (1): 83–86. https://doi.org/10.1162/106454602753694774 Cite Icon Cite Permissions Share Icon Share MailTo Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Eleonora Bilotta, Eduardo Reck Miranda, Pietro Pantano, Peter M. Todd; Artificial Life Models for Musical Applications: Workshop Report. Artif Life 2002; 8 (1): 83–86. doi: https://doi.org/10.1162/106454602753694774 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2002 Massachusetts Institute of Technology2002 Article PDF first page preview Close Modal You do not currently have access to this content.