Behavioral Specialization in Embodied Evolutionary Robotics: Why So Difficult?

Embodied evolutionary robotics is an on-line distributed learning method used in collective robotics where robots are facing open environments. This paper focuses on learning behavioral specialization, as defined by robots being able to demonstrate different kind of behaviors at the same time (e.g., division of labor). Using a foraging task with two resources available in limited quantities, we show that behavioral specialization is unlikely to evolve in the general case, unless very specific conditions are met regarding interactions between robots (a very sparse communication network is required) and the expected outcome of specialization (specialization into groups of similar sizes is easier to achieve). We also show that the population size (the larger the better) as well as the selection scheme used (favoring exploration over exploitation) both play important – though not always mandatory – roles. This research sheds light on why existing embodied evolution algorithms are limited with respect to learning efficient division of labor in the general case, i.e., where it is not possible to guess before deployment if behavioral specialization is required or not, and gives directions to overcome current limitations.This work is supported by the European Unions Horizon 2020 research and innovation programme under grant agreement No 640891, and the ERC Advanced Grant EPNet (340828). Part of the experiments presented in this paper were carried out using the Grid’5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER, and several Universities as well as other funding bodies (see https://www.grid5000.fr). The other parts of the simulations have been done in the supercomputer MareNostrum at Barcelona Supercomputing Center – Centro Nacional de Supercomputacion (The Spanish National Supercomputing Center).Peer ReviewedPostprint (published version

Modelling the co-evolution of trade and culture

We presents a new framework to study the co-evolution of cultural change and trade. The design aims for a trade-off between the flexibility necessary for the implementation of multiple models and the structure necessary for the comparison between the models implemented. To create this framework we propose an Agent-Based Model relying on agents producing, exchanging and associating values to a list of goods. We present the key concepts of the framework and two examples of its implementation which allow us to show the flexibility of our framework. Moreover, we compare the results obtained by the two models, thus validating the structure of the framework. Finally, we validate the implementation of a trading model by studying the price structure it produces

Evolution of Heterogeneous Cellular Automata in Fluctuating Environments

The importance of environmental fluctuations in the evolution of living organisms by natural selection has been widely noted by biologists and linked to many important characteristics of life such as modularity, plasticity, genotype size, mutation rate, learning, or epigenetic adaptations. In artificial-life simulations, however, environmental fluctuations are usually seen as a nuisance rather than an essential characteristic of evolution. HetCA is a heterogeneous cellular automata characterized by its ability to generate open-ended long-term evolution and “evolutionary progress”. In this paper, we propose to measure the impact of different types of environmental fluctuations in HetCA. Our results indicate that environmental changes induce mechanisms analogous to epigenetic adaptation or multilevel selection. This is particularly prevalent in two of the tested fluctuation schemes, which involve a round-robin inhibition of certain cell types, where phenotypic selection seems to occur.Funding for this work was provided by the Science Foundation Ireland and the ERC Advanced Grant EPNet #340828. Some of the simulations were run on the MareNostrum supercomputer of the Barcelona Supercomputing Center.Postprint (author's final draft

How social learning shapes the efficacy of preventative health behaviors in an outbreak.

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior

Modelling the co-evolution of trade and culture in past societies

This paper presents a new framework to study the co-evolution of cultural change and trade. The design aims for a trade-off between the flexibility necessary for the implementation of multiple models and the structure necessary for the comparison between the models implemented. To create this framework we propose an Agent-Based Model relying on agents producing, exchanging and associating values to a list of goods. We present the key concepts of the framework and two examples of its implementation which allow us to show the flexibility of our framework. Moreover, we compare the results obtained by the two models, thus validating the structure of the framework. Finally, we validate the implementation of a trading model by studying the price structure it produces.Funding for this work was provided by the ERC Advanced Grant EPNet (340828) and the SimulPast Consolider Ingenio project (CSD2010-00034) of the former Ministry for Science and Innovation of the Spanish Government. The model was created using Pandora (Rubio-Campillo 2014). R was used for figures and statistical analysis (R Development Team 2012). The estimation of the a parameters have been computed with the R package poweRlaw (Gillespie 2015). The simulations have been done in the supercomputer MareNostrum at Barcelona Supercomputing Center - Centro Nacional de Supercomputación (The Spanish National Supercomputing Center). The source code of the model is licensed under a GNU General Public License.Peer ReviewedPostprint (author's final draft

A partial prehistory of the Southwest Silk Road: Archaeometallurgical networks along the sub-Himalayan corridor

Historical phenomena often have prehistoric precedents, with this paper we investigate the potential for archaeometallurgical analyses and networked data processing to elucidate the progenitors of the Southwest Silk Road in Mainland Southeast Asia and southern China. We present original microstructural, elemental and lead isotope data for 40 archaeological copperbase metal samples, mostly from the UNESCO-listed site of Halin, and lead isotope data for 25 geological copper-mineral samples, also from Myanmar. We combined these data with existing datasets (N=98 total) and compared them to the 1000+ sample late prehistoric archaeometallurgical database available from Cambodia, Laos, Thailand, Vietnam and Yunnan. Lead isotope data, contextualised for alloy, find location and date, were interpreted manually for intra-site, inter-site and inter-regional consistency, which hint at significant multi-scalar connectivity from the late 2nd millennium BC. To test this interpretation statistically, the archaeological lead isotope data were then processed using regionally-adapted productionderived consistency parameters. Complex networks analysis using the Leiden community detection algorithm established groups of artefacts sharing lead isotopic consistency. Introducing the geographic component allowed for the identification of communities of sites with consistent assemblages. The four major communities were consistent with the manually interpreted exchange networks and suggest southern sections of the Southwest Silk Road were active in the late 2nd millennium BC

Content-dependent biases in social learning strategies : a multiscale approach

In this thesis, we have quantified the influence of content-dependent biases in social learning strategies. Our theoretical framework combines agent-based models and Bayesian inference to measure content-dependent biases in large-scale social learning strategies. Our first empirical study measures the impact of social transmission biases in Twitter. The novelty of the second study is two-fold: ours is one of the rare uses of computational modelling in historical Roman Studies and one of the first tests of the impact of success bias across large spatial and temporal scales. El contenido de lo que aprendemos socialmente moldea la evolución de la cultura humana. En esta tesis hemos cuantificado la influencia de diferentes estrategias de aprendizaje social analizando procesos culturales en diferentes escalas. Se propone un marco teórico que combina los modelos basados en agentes y la inferencia bayesiana para detectar sesgos dependientes de contenido en la evolución cultural. El análisis se realizará sobre tres escenarios diferentes: un escenario teórico, que revela el potencial del sesgo de éxito, y dos casos de estudio empirico que representan distintas escalas espacio-temporales. En el primer caso, se estudia la influencia de transmisión social dependiendo del contenido de diferentes clases de noticias online, mientras que en el segundo se analiza la influencia de los sesgos de éxito en los cambios de distribución de vajillas en el este del Imperio Romano.The content of what we learn shapes the evolution of human culture and society. In this thesis, we have quantified the influence of content-dependent biases in social learning strategies. Our theoretical framework combines agent-based models and Bayesian inference to measure content-dependent biases in large-scale social learning strategies. Our first empirical study measures the impact of social transmission biases in Twitter. The novelty of the second study is two-fold: ours is one of the rare uses of computational modelling in historical Roman Studies and one of the first tests of the impact of success bias across large spatial and temporal scales

Data and Code for the paper How Social Learning Shapes the Efficacy of Preventative Health Behaviors in an Outbreak

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior