02220nas a2200145 4500008004100000245010000041210006900141300001100210490000900221520172400230100002101954700002301975700002801998856004802026 2020 eng d00aForecasting of Population Narcotization under the Implementation of a Drug Use Reduction Policy0 aForecasting of Population Narcotization under the Implementation a1–140 v20203 aIn this paper, we present an approach to drug addiction simulation and forecasting in the medium and long terms in cities having a high population density and a high rate of social communication. Drug addiction forecasting is one of the basic components of the antidrug policy, giving informational and analytic support both at the regional and at the governmental level. However, views on the drug consumption problem vary in different regions, and as a consequence, several approaches to antidrug policy implementation exist. Thereby, notwithstanding the fact that the phenomenology of the population narcotization process is similar in the different regions, approaches to the modeling of drug addiction may also substantially differ for different kinds of antidrug policies. This paper presents a survey of the available antidrug policies and the corresponding approaches to the simulation of population narcotization. This article considers the approach to the construction of the regression model of anesthesia on the main components formed on the basis of indicators of social and economic development. The substantiation of the chosen method is given, which is associated with a significant correlation of indicators, which characterizes the presence of a small number of superfactors. This allows us to form a conclusion about the general level of development of the region as the main factor determining the drug addiction. A new model is proposed for one of the most widespread antidrug policies, namely, the drug use reduction policy. The model helps determine the significant factors of population narcotization and allows to estimate its damage. The model is tested successfully using St. Petersburg data.1 aMityagin, Sergey1 aGershenson, Carlos1 aBoukhanovsky, Alexander uhttp://turing.iimas.unam.mx/sos/?q=node/18801394nas a2200241 4500008004100000245012100041210006900162520060000231100002400831700001900855700002300874700002200897700002300919700001600942700002100958700002000979700002100999700002001020700001901040700003201059700001901091856004201110 2019 eng d00aComplexity Explained: A Grassroot Collaborative Initiative to Create a Set of Essential Concepts of Complex Systems.0 aComplexity Explained A Grassroot Collaborative Initiative to Cre3 aComplexity science, also called complex systems science, studies how a large collection of components – locally interacting with each other at small scales – can spontaneously self-organize to exhibit non-trivial global structures and behaviors at larger scales, often without external intervention, central authorities or leaders. The properties of the collection may not be understood or predicted from the full knowledge of its constituents alone. Such a collection is called a complex system and it requires new mathematical frameworks and scientific methodologies for its investigation.1 aDe Domenico, Manlio1 aCamargo, Chico1 aGershenson, Carlos1 aGoldsmith, Daniel1 aJeschonnek, Sabine1 aKay, Lorren1 aNichele, Stefano1 aNicolás, José1 aSchmickl, Thomas1 aStella, Massimo1 aBrandoff, Josh1 aSalinas, Ángel, José Mart1 aSayama, Hiroki uhttps://complexityexplained.github.io01843nas a2200193 4500008004100000022001400041245009400055210006900149260001300218300001400231490000700245520123600252100002401488700002101512700002101533700002101554700002301575856005101598 2018 eng d a0167-739X00aMultimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics0 aMultimodel agentbased simulation environment for massgatherings cFebruary a155–1650 v793 aAbstract The increasing interest in complex phenomena, especially in crowd and pedestrian dynamics, has conditioned the demand not only for more sophisticated autonomous models but also for mechanisms that would bring these models together. This paper presents a multimodel agent-based simulation technique based on the incorporation of multiple modules. Two key principles are presented to guide this integration: a common abstract space where entities of different models interact, and commonly controlled agents–-abstract actors operating in the common space, which can be handled by different agent-based models. In order to test the proposed methodology, we run a set of simulations of cinema building evacuation using the general-purpose {PULSE} simulation environment. In this paper we utilize crowd pressure as a metric to estimate the capacity of different emergent conditions to traumatically affect pedestrians in the crowd. The proposed approach is evaluated through a series of experiments simulating the emergency evacuation from a cinema building to the city streets, where building and street levels are reproduced in heterogeneous models. This approach paves the way for modeling realistic city-wide evacuations.1 aKarbovsk, Vladislav1 aVoloshin, Daniil1 aKarsakov, Andrey1 aBezgodov, Alexey1 aGershenson, Carlos uhttp://dx.doi.org/10.1016/j.future.2016.10.00200564nam a2200145 4500008004100000245011200041210006900153260003300222100002500255700002300280700001500303700001900318700002000337856006100357 2018 eng d00aUnifying Themes in Complex Systems IX: Proceedings of the Ninth International Conference on Complex Systems0 aUnifying Themes in Complex Systems IX Proceedings of the Ninth I aCambridge, MA, USAbSpringer1 aMorales, Alfredo, J.1 aGershenson, Carlos1 aBraha, Dan1 aMinai, Ali, A.1 aBar-Yam, Yaneer uhttps://link.springer.com/book/10.1007/978-3-319-96661-800431nas a2200109 4500008004100000245011300041210006900154100001700223700002100240700002300261856003700284 2017 eng d00aSelf-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications0 aSelfOrganization in Traffic Lights Evolution of Signal Control w1 aGoel, Sanjay1 aBush, Stephen, F1 aGershenson, Carlos uhttps://arxiv.org/abs/1708.0718801826nas a2200181 4500008004100000022001400041245009400055210006900149300000600218520123800224653002101462100002401483700002101507700002101528700002101549700002301570856005101593 2016 eng d a0167-739X00aMultimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics0 aMultimodel agentbased simulation environment for massgatherings a-3 aAbstract The increasing interest in complex phenomena, especially in crowd and pedestrian dynamics, has conditioned the demand not only for more sophisticated autonomous models but also for mechanisms that would bring these models together. This paper presents a multimodel agent-based simulation technique based on the incorporation of multiple modules. Two key principles are presented to guide this integration: a common abstract space where entities of different models interact, and commonly controlled agents–-abstract actors operating in the common space, which can be handled by different agent-based models. In order to test the proposed methodology, we run a set of simulations of cinema building evacuation using the general-purpose \{PULSE\} simulation environment. In this paper we utilize crowd pressure as a metric to estimate the capacity of different emergent conditions to traumatically affect pedestrians in the crowd. The proposed approach is evaluated through a series of experiments simulating the emergency evacuation from a cinema building to the city streets, where building and street levels are reproduced in heterogeneous models. This approach paves the way for modeling realistic city-wide evacuations.10aUrgent computing1 aKarbovsk, Vladislav1 aVoloshin, Daniil1 aKarsakov, Andrey1 aBezgodov, Alexey1 aGershenson, Carlos uhttp://dx.doi.org/10.1016/j.future.2016.10.00200477nas a2200133 4500008004100000245005800041210005800099260004200157300001400199100002300213700002900236700003000265856004800295 2015 eng d00aHacia un sistema de salud autoorganizante y emergente0 aHacia un sistema de salud autoorganizante y emergente aMexicobAcademia Nacional de Medicina a245–2541 aGershenson, Carlos1 aBarajas, Enrique, Ruelas1 aCorona, Ricardo, Mansilla uhttp://turing.iimas.unam.mx/sos/?q=node/15201331nas a2200169 4500008004100000245004100041210004100082260000700123300001300130490000700143520087700150100002001027700001901047700002301066700001901089856005301108 2015 eng d00aUrban Transfer Entropy across Scales0 aUrban Transfer Entropy across Scales c07 ae01337800 v103 a
The morphology of urban agglomeration is studied here in the context of information exchange between different spatio-temporal scales. Urban migration to and from cities is characterised as non-random and following non-random pathways. Cities are multidimensional non-linear phenomena, so understanding the relationships and connectivity between scales is important in determining how the interplay of local/regional urban policies may affect the distribution of urban settlements. In order to quantify these relationships, we follow an information theoretic approach using the concept of Transfer Entropy. Our analysis is based on a stochastic urban fractal model, which mimics urban growing settlements and migration waves. The results indicate how different policies could affect urban morphology in terms of the information generated across geographical scales.
1 aMurcio, Roberto1 aMorphet, Robin1 aGershenson, Carlos1 aBatty, Michael uhttp://dx.doi.org/10.1371%2Fjournal.pone.013378000372nas a2200121 4500008004100000245003400041210003300075260002800108100002300136700001900159700002400178856004800202 2014 eng d00aDolor, placebos y complejidad0 aDolor placebos y complejidad aMexicobEditorial Alfil1 aGershenson, Carlos1 aRosado, Javier1 aBistre-Cohén, Sara uhttp://turing.iimas.unam.mx/sos/?q=node/15301372nas a2200157 4500008004100000022001400041245005300055210004700108490000600155520086900161100001901030700003401049700001601083700002301099856009201122 2014 eng d a2296-914400aThe Past, Present, and Future of Artificial Life0 aPast Present and Future of Artificial Life0 v13 aFor millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into fourteen themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields.
1 aAguilar, Wendy1 aBonfil, Guillermo, Santamarí1 aFroese, Tom1 aGershenson, Carlos uhttp://www.frontiersin.org/computational_intelligence/10.3389/frobt.2014.00008/abstract01219nas a2200157 4500008004100000020002200041245004900063210004700112260003200159300000900191520077000200100002300970700001500993700001801008856003501026 2013 eng d a978-3-642-32816-900aFacing Complexity: Prediction vs. Adaptation0 aFacing Complexity Prediction vs Adaptation aBerlin HeidelbergbSpringer a3-143 aOne of the presuppositions of science since the times of Galileo, Newton, Laplace, and Descartes has been the predictability of the world. This idea has strongly influenced scientific and technological models. However, in recent decades, chaos and complexity have shown that not every phenomenon is predictable, even if it is deterministic. If a problem space is predictable, in theory we can find a solution via optimization. Nevertheless, if a problem space is not predictable, or it changes too fast, very probably optimization will offer obsolete solutions. This occurs often when the immediate solution affects the problem itself. An alternative is found in adaptation. An adaptive system will be able to find by itself new solutions for unforeseen situations.1 aGershenson, Carlos1 aMassip, A.1 aBastardas, A. uhttp://arxiv.org/abs/1112.384301212nas a2200169 4500008004100000245003800041210003400079260003200113300001200145490000800157520076500165100002300930700001500953700001500968700002400983856003501007 2012 eng d00aThe World as Evolving Information0 aWorld as Evolving Information aBerlin HeidelbergbSpringer a100-1150 vVII3 aThis paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents five tentative laws of information, valid at multiple scales, which are generalizations of Darwinian, cybernetic, thermodynamic, and complexity principles. These are further used to discuss the notions of life and cognition and their evolution.1 aGershenson, Carlos1 aMinai, Ali1 aBraha, Dan1 aBar-Yam}, Yaneer, { uhttp://arxiv.org/abs/0704.030401537nas a2200181 4500008004100000245008900041210006900130260003200199300001200231490000600243520096500249100002301214700002301237700001501260700001501275700002401290856004101314 2011 eng d00aProtocol Requirements for Self-Organizing Artifacts: Towards an Ambient Intelligence0 aProtocol Requirements for SelfOrganizing Artifacts Towards an Am aBerlin HeidelbergbSpringer a136-1430 vV3 aWe discuss which properties common-use artifacts should have to collaborate without human intervention. We conceive how devices, such as mobile phones, PDAs, and home appliances, could be seamlessly integrated to provide an "ambient intelligence" that responds to the users desires without requiring explicit programming or commands. While the hardware and software technology to build such systems already exists, yet there is no protocol to direct and give meaning to their interactions. We propose the first steps in the development of such a protocol, which would need to be adaptive, extensible, and open to the community, while promoting self-organization. We argue that devices, interacting through "game-like" moves, can learn to agree about how to communicate, with whom to cooperate, and how to delegate and coordinate specialized tasks. Like this, they may evolve distributed cognition or collective intelligence able to tackle any complex of tasks.1 aGershenson, Carlos1 aHeylighen, Francis1 aMinai, Ali1 aBraha, Dan1 aBar-Yam}, Yaneer, { uhttp://arxiv.org/abs/nlin.AO/040400401145nam a2200145 4500008004100000020002200041245014200063210006900205260001300274490000900287520060500296100002700901700002300928856004800951 2011 eng d a978-3-642-19166-400aSelf-Organizing Systems 5th International Workshop, IWSOS 2011, Karlsruhe, Germany, February 23-24, 2011, Proceedings. Springer LNCS 65570 aSelfOrganizing Systems 5th International Workshop IWSOS 2011 Kar bSpringer0 v65573 aThis book constitutes the refereed proceedings of the 5th International Workshop on Self-Organizing Systems, IWSOS 2011, held in Karlsruhe, Germany, in February 2011. The 9 revised full papers presented together with 1 invited paper were carefully selected from 25 initial submissions. It was the 5th workshop in a series of multidisciplinary events dedicated to self-organization in networked systems with main focus on communication and computer networks. The papers address theoretical aspects of self-organization as well as applications in communication and computer networks and robot networks.1 aBettstetter, Christian1 aGershenson, Carlos uhttp://dx.doi.org/10.1007/978-3-642-19167-100440nas a2200157 4500008004100000245003000041210003000071260003300101300001200134100002300146700001900169700002300188700001400211700001800225856003900243 2007 eng d00aComplexity and Philosophy0 aComplexity and Philosophy aOxfordbRadcliffe Publishing a117-1341 aHeylighen, Francis1 aCilliers, Paul1 aGershenson, Carlos1 aBogg, Jan1 aGeyer, Robert uhttp://arxiv.org/abs/cs.CC/060407201369nas a2200181 4500008004100000245006400041210006300105260002600168520081100194100002501005700002601030700002601056700002301082700001801105700001701123700001701140856003001157 2007 eng d00aSmartocracy: Social Networks for Collective Decision Making0 aSmartocracy Social Networks for Collective Decision Making bIEEE Computer Society3 aSmartocracy is a social software system for collec- tive decision making. The system is composed of a social network that links individuals to those they trust to make good decisions and a decision network that links individuals to their voted-on solutions. Such networks allow a variety of algorithms to convert the link choices made by individual participants into specific decision outcomes. Simply interpreting the linkages differently (e.g. ignoring trust links, or using them to weight an individual's vote) provides a variety of outcomes fit for different decision making scenarios. This paper will discuss the Smartocracy network data structures, the suite of collective decision making algorithms currently supported, and the results of two collective decisions regarding the design of the system.1 aRodriguez, Marko, A.1 aSteinbock, Daniel, J.1 aWatkins, Jennifer, H.1 aGershenson, Carlos1 aBollen, Johan1 aGrey, Victor1 adeGraf, Brad uhttp://tinyurl.com/ybojp800439nas a2200121 4500008004100000245007200041210006900113260003300182100002300215700001400238700001800252856004700270 2007 eng d00aTowards a General Methodology for Designing Self-Organizing Systems0 aTowards a General Methodology for Designing SelfOrganizing Syste aOxfordbRadcliffe Publishing1 aGershenson, Carlos1 aBogg, Jan1 aGeyer, Robert uhttp://turing.iimas.unam.mx/sos/?q=node/4901643nas a2200217 4500008004100000245007400041210006800115260001400183300001200197520099000209100002301199700002501222700002101247700001801268700001901286700001801305700001701323700002201340700001901362856004401381 2006 eng d00aThe Role of Redundancy in the Robustness of Random {Boolean} Networks0 aRole of Redundancy in the Robustness of Random Boolean Networks bMIT Press a35–423 aEvolution depends on the possibility of successfully exploring fitness landscapes via mutation and recombination. With these search procedures, exploration is difficult in "rugged" fitness landscapes, where small mutations can drastically change functionalities in an organism. Random Boolean networks (RBNs), being general models, can be used to explore theories of how evolution can take place in rugged landscapes; or even change the landscapes. In this paper, we study the effect that redundant nodes have on the robustness of RBNs. Using computer simulations, we have found that the addition of redundant nodes to RBNs increases their robustness. We conjecture that redundancy is a way of "smoothening" fitness landscapes. Therefore, redundancy can facilitate evolutionary searches. However, too much redundancy could reduce the rate of adaptation of an evolutionary process. Our results also provide supporting evidence in favour of Kauffman's conjecture (Kauffman, 2000, p.195).1 aGershenson, Carlos1 aKauffman, Stuart, A.1 aShmulevich, Ilya1 aRocha, L., M.1 aYaeger, L., S.1 aBedau, M., A.1 aFloreano, D.1 aGoldstone, R., L.1 aVespignani, A. uhttp://uk.arxiv.org/abs/nlin.AO/051101801234nas a2200181 4500008004100000245004600041210004400087260001500131300001400146520075300160100002300913700001400936700001700950700001500967700001400982700001500996856004101011 2004 eng d00aIntroduction to Random {Boolean} Networks0 aIntroduction to Random Boolean Networks aBoston, MA a160–1733 aThe goal of this tutorial is to promote interest in the study of random Boolean networks (RBNs). These can be very interesting models, since one does not have to assume any functionality or particular connectivity of the networks to study their generic properties. Like this, RBNs have been used for exploring the configurations where life could emerge. The fact that RBNs are a generalization of cellular automata makes their research a very important topic. The tutorial, intended for a broad audience, presents the state of the art in RBNs, spanning over several lines of research carried out by different groups. We focus on research done within artificial life, as we cannot exhaust the abundant research done over the decades related to RBNs.1 aGershenson, Carlos1 aBedau, M.1 aHusbands, P.1 aHutton, T.1 aKumar, S.1 aSuzuki, H. uhttp://arxiv.org/abs/nlin.AO/040800601111nas a2200181 4500008004100000245007400041210006900115260001400184300001400198520057100212100002300783700001600806700001400822700001700836700001600853700001900869856004100888 2004 eng d00aUpdating Schemes in Random {Boolean} Networks: Do They Really Matter?0 aUpdating Schemes in Random Boolean Networks Do They Really Matte bMIT Press a238–2433 aIn this paper we try to end the debate concerning the suitability of different updating schemes in random Boolean networks (RBNs). We quantify for the first time loose attractors in asyncrhonous RBNs, which allows us to analyze the complexity reduction related to different updating schemes. We also report that all updating schemes yield very similar critical stability values, meaning that the "edge of chaos" does not depend much on the updating scheme. After discussion, we conclude that synchonous RBNs are justifiable theoretical models of biological networks.1 aGershenson, Carlos1 aPollack, J.1 aBedau, M.1 aHusbands, P.1 aIkegami, T.1 aWatson, R., A. uhttp://arxiv.org/abs/nlin.AO/040200601425nas a2200205 4500008004100000245004100041210003900082260002000121300001400141520087400155100002301029700001901052700002001071700001501091700002001106700001701126700001601143700001601159856004401175 2003 eng d00aContextual Random {Boolean} Networks0 aContextual Random Boolean Networks bSpringer-Verlag a615–6243 aWe propose the use of Deterministic Generalized Asynchronous Random Boolean Networks (Gershenson, 2002) as models of contextual deterministic discrete dynamical systems. We show that changes in the context have drastic effects on the global properties of the same networks, namely the average number of attractors and the average percentage of states in attractors. We introduce the situation where we lack knowledge on the context as a more realistic model for contextual dynamical systems. We notice that this makes the network non-deterministic in a specific way, namely introducing a non-Kolmogorovian quantum-like structure for the modelling of the network (Aerts 1986). In this case, for example, a state of the network has the potentiality (probability) of collapsing into different attractors, depending on the specific form of lack of knowledge on the context.1 aGershenson, Carlos1 aBroekaert, Jan1 aAerts, Diederik1 aBanzhaf, W1 aChristaller, T.1 aDittrich, P.1 aKim, J., T.1 aZiegler, J. uhttp://uk.arxiv.org/abs/nlin.AO/030302101287nas a2200193 4500008004100000245004700041210004500088260002100133300001400154520075400168100002300922700002300945700001500968700002000983700001701003700001601020700001601036856004101052 2003 eng d00aWhen Can We Call a System Self-Organizing?0 aWhen Can We Call a System SelfOrganizing aBerlinbSpringer a606–6143 aWe do not attempt to provide yet another definition of self-organizing systems, nor review previous definitions. We explore the conditions necessary to describe self-organizing systems, inspired on decades of their study, in order to understand them better. These involve the dynamics of the system, and the purpose, boundaries, and description level chosen by an observer. We show how, changing the level or ``graining'' of description, the same system can be self-organizing or not. We also discuss common problems we face when studying self-organizing systems. We analyse when building, designing, and controlling artificial self-organizing systems is useful. We state that self-organization is a way of observing systems, not a class of systems.1 aGershenson, Carlos1 aHeylighen, Francis1 aBanzhaf, W1 aChristaller, T.1 aDittrich, P.1 aKim, J., T.1 aZiegler, J. uhttp://arxiv.org/abs/nlin.AO/030302001233nas a2200193 4500008004100000245008600041210006900127260005900196300001200255490000700267520056100274100002300835700002800858700002100886700001700907700001900924700002500943856007100968 2002 eng d00aBehaviour-Based Knowledge Systems: An Epigenetic Path from Behaviour to Knowledge0 aBehaviourBased Knowledge Systems An Epigenetic Path from Behavio aEdinburgh, ScotlandbLund University Cognitive Studies a35–410 v943 aIn this paper we expose the theoretical background underlying our current research. This consists in the development of behaviour-based knowledge systems, for closing the gaps between behaviour-based and knowledge-based systems, and also between the understandings of the phenomena they model. We expose the requirements and stages for developing behaviour-based knowledge systems and discuss their limits. We believe that these are necessary conditions for the development of higher order cognitive capacities, in artificial and natural cognitive systems.1 aGershenson, Carlos1 aPrince, Christopher, G.1 aDemiris, Yiannis1 aMarom, Yuval1 aKozima, Hideki1 aBalkenius, Christian uhttp://www.lucs.lu.se/ftp/pub/LUCS%5FStudies/LUCS94/Gershenson.pdf01283nas a2200157 4500008004100000245004800041210004600089260003400135300001000169520082900179100002301008700002101031700001801052700001901070856003601089 2002 eng d00aClassification of Random {Boolean} Networks0 aClassification of Random Boolean Networks aCambridge, MA, USAbMIT Press a1–83 aWe provide the first classification of different types of Random Boolean Networks (RBNs). We study the differences of RBNs depending on the degree of synchronicity and determinism of their updating scheme. For doing so, we first define three new types of RBNs. We note some similarities and differences between different types of RBNs with the aid of a public software laboratory we developed. Particularly, we find that the point attractors are independent of the updating scheme, and that RBNs are more different depending on their determinism or non-determinism rather than depending on their synchronicity or asynchronicity. We also show a way of mapping non-synchronous deterministic RBNs into synchronous RBNs. Our results are important for justifying the use of specific types of RBNs for modelling natural phenomena.1 aGershenson, Carlos1 aStandish, R., K.1 aBedau, M., A.1 aAbbass, H., A. uhttp://arxiv.org/abs/cs/020800101168nas a2200193 4500008004100000245010800041210006900149260004000218300001400258490000900272520053200281100002200813700001600835700001700851700002000868700001600888700002800904856004200932 2000 eng d00aA Model for Combination of External and Internal Stimuli in the Action Selection of an Autonomous Agent0 aModel for Combination of External and Internal Stimuli in the Ac aAcapulco, MéxicobSpringer, Verlag a621–6330 v17933 aThis paper proposes a model for combination of external and internal stimuli for the action selection in an autonomous agent, based in an action selection mechanism previously proposed by the authors. This combination model includes additive and multiplicative elements, which allows to incorporate new properties, which enhance the action selection. A given parameter a, which is part of the proposed model, allows to regulate the degree of dependence of the observed external behaviour from the internal states of the entity.1 aGonzález, P., P.1 aNegrete, J.1 aBarreiro, A.1 aGershenson., C.1 aCairó, {O.1 aSúcar, F.J., Cantú L. uhttp://uk.arxiv.org/abs/cs.AI/021104001066nas a2200193 4500008004100000245006300041210006200104260003000166520046800196100001900664700002200683700001600705700002300721700001900744700002000763700002600783700002400809856003900833 2000 eng d00aThinking Adaptive: Towards a Behaviours Virtual Laboratory0 aThinking Adaptive Towards a Behaviours Virtual Laboratory aParis, FrancebISAB press3 aIn this paper we name some of the advantages of virtual laboratories; and propose that a Behaviours Virtual Laboratory should be useful for both biologists and AI researchers, offering a new perspective for understanding adaptive behaviour. We present our development of a Behaviours Virtual Laboratory, which at this stage is focused in action selection, and show some experiments to illustrate the properties of our proposal, which can be accessed via Internet.1 aGershenson, C.1 aGonzález, P., P.1 aNegrete, J.1 aMeyer, Jean-Arcady1 aBerthoz, Alain1 aFloreano, Dario1 aRoitblat, Herbert, L.1 aWilson, Stewart, W. uhttp://uk.arxiv.org/abs/cs/0211028