Thomas Gabor;Steffen Illium;Maximilian Zorn;Cristian Lenta;Andy Mattausch;Lenz Belzner;Claudia Linnhoff-Popien
A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this work’s previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.
{"title":"Self-Replication in Neural Networks","authors":"Thomas Gabor;Steffen Illium;Maximilian Zorn;Cristian Lenta;Andy Mattausch;Lenz Belzner;Claudia Linnhoff-Popien","doi":"10.1162/artl_a_00359","DOIUrl":"10.1162/artl_a_00359","url":null,"abstract":"A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators’ robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this work’s previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40139127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.
我们进化浮点六分多项式种群遗传规划二叉树为多达一百万代。我们观察到持续的创新，但这受到树的深度的限制。我们认为深层表达对学习是有弹性的，因为它们分散了信息，阻碍了高度嵌套生物体的可进化性和适应性，我们主张开放的复杂性。具有超过20亿条指令(深度20,000)的程序是通过交叉创建的。为了支持遗传编程(GP)的无限长期进化实验，我们使用增量适应度评估和SIMD并行AVX 512位指令和16个线程，在Intel Xeon Gold 6136 CPU 3.00GHz服务器上产生相当于每秒1.1万亿GP操作，1.1兆gpop的性能。
{"title":"Long-Term Evolution Experiment with Genetic Programming","authors":"William B. Langdon;Wolfgang Banzhaf","doi":"10.1162/artl_a_00360","DOIUrl":"10.1162/artl_a_00360","url":null,"abstract":"We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by tree depth. We suggest that deep expressions are resilient to learning as they disperse information, impeding evolvability, and the adaptation of highly nested organisms, and we argue instead for open complexity. Programs with more than 2,000,000,000 instructions (depth 20,000) are created by crossover. To support unbounded long-term evolution experiments in genetic programming (GP), we use incremental fitness evaluation and both SIMD parallel AVX 512-bit instructions and 16 threads to yield performance equivalent to 1.1 trillion GP operations per second, 1.1 tera GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40140869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collecting random samples produced by a predefined distribution of gene activation patterns. This sampling method introduces stochasticity, in turn inducing dynamicity. This dynamicity is imposed on top of an already complex fitness landscape. So where sampling is used, it is important to understand which effects arise from the structure of the fitness landscape, and which arise from the dynamicity imposed on it. Stochasticity of the fitness function also causes difficulties in reproducibility and in post-experimental analyses. We develop a deterministic distributional fitness evaluation by considering the complete distribution of gene activity patterns, so as to avoid stochasticity in fitness assessment. This fitness evaluation facilitates repeatability. Its determinism permits us to ascertain theoretical bounds on the fitness, and thus to identify whether the algorithm has reached a global optimum. It enables us to differentiate the effects of the problem domain from those of the noisy fitness evaluation, and thus to resolve two remaining anomalies in the behaviour of the problem domain of Espinosa-Soto and A. Wagner (2010). We also reveal some properties of solution GRNs that lead them to be robust and modular, leading to a deeper understanding of the nature of the problem domain. We conclude by discussing potential directions toward simulating and understanding the emergence of modularity in larger, more complex domains, which is key both to generating more useful modular solutions, and to understanding the ubiquity of modularity in biological systems.
{"title":"Resolving Anomalies in the Behaviour of a Modularity-Inducing Problem Domain with Distributional Fitness Evaluation","authors":"Zhenyue Qin;Tom Gedeon;R. I. McKay","doi":"10.1162/artl_a_00353","DOIUrl":"10.1162/artl_a_00353","url":null,"abstract":"Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collecting random samples produced by a predefined distribution of gene activation patterns. This sampling method introduces stochasticity, in turn inducing dynamicity. This dynamicity is imposed on top of an already complex fitness landscape. So where sampling is used, it is important to understand which effects arise from the structure of the fitness landscape, and which arise from the dynamicity imposed on it. Stochasticity of the fitness function also causes difficulties in reproducibility and in post-experimental analyses. We develop a deterministic distributional fitness evaluation by considering the complete distribution of gene activity patterns, so as to avoid stochasticity in fitness assessment. This fitness evaluation facilitates repeatability. Its determinism permits us to ascertain theoretical bounds on the fitness, and thus to identify whether the algorithm has reached a global optimum. It enables us to differentiate the effects of the problem domain from those of the noisy fitness evaluation, and thus to resolve two remaining anomalies in the behaviour of the problem domain of Espinosa-Soto and A. Wagner (2010). We also reveal some properties of solution GRNs that lead them to be robust and modular, leading to a deeper understanding of the nature of the problem domain. We conclude by discussing potential directions toward simulating and understanding the emergence of modularity in larger, more complex domains, which is key both to generating more useful modular solutions, and to understanding the ubiquity of modularity in biological systems.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39910642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This special issue highlights key selections from the 2019 Conference on Artificial Life, ALIFE’19, hosted by Newcastle University in Newcastle upon Tyne, UK. The annual conference addresses the synthesis and simulation of living systems. The
{"title":"Editorial: The 2019 Conference on Artificial Life Special Issue","authors":"Harold Fellermann;Rudolf M. Füchslin","doi":"10.1162/artl_e_00380","DOIUrl":"10.1162/artl_e_00380","url":null,"abstract":"This special issue highlights key selections from the 2019 Conference on Artificial Life, ALIFE’19, hosted by Newcastle University in Newcastle upon Tyne, UK. The annual conference addresses the synthesis and simulation of living systems. The","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49195582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots” lays out a vision for appreciating sex robots in aesthetic terms, centering the concept of “beauty” as a measure of what they can inspire culturally and existentially. In these comments I turn toward the field of human-robot interaction and the ethical challenges that inhabit the core of such an aesthetic turn.
{"title":"Comment on Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots”","authors":"Thomas Arnold","doi":"10.1162/artl_a_00362","DOIUrl":"10.1162/artl_a_00362","url":null,"abstract":"Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots” lays out a vision for appreciating sex robots in aesthetic terms, centering the concept of “beauty” as a measure of what they can inspire culturally and existentially. In these comments I turn toward the field of human-robot interaction and the ethical challenges that inhabit the core of such an aesthetic turn.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49220295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oskar Elek;Joseph N. Burchett;J. Xavier Prochaska;Angus G. Forbes
We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones’s (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones’s work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM’s self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.
{"title":"Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks","authors":"Oskar Elek;Joseph N. Burchett;J. Xavier Prochaska;Angus G. Forbes","doi":"10.1162/artl_a_00351","DOIUrl":"10.1162/artl_a_00351","url":null,"abstract":"We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones’s (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones’s work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM’s self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6720217/9930987/09931049.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39723623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Review of Art in the Age of Machine Learning by Sofian Audry","authors":"Simon Penny","doi":"10.1162/artl_r_00352","DOIUrl":"10.1162/artl_r_00352","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44446348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this issue we are pleased to share with you a diverse set of reading materials. Sadly, we mark with an obituary the passing of Julian Miller, a researcher whose loss has been keenly felt within the community of Artificial Life researchers. He shall be sorely missed. On a much brighter note, the second installment of Chris Adami’s column exploring how artificial evolution might facilitate the design of General Intelligence is to be found within the pages of this issue. Adami explains how the indirect encoding of artificial brains to facilitate neuro-evolution might be managed. He discusses approaches to choosing an appropriate neuron, how to connect neurons to create a functioning network, how to train the network, and how the different options scale up to high levels of complexity. Drawing such connections between the techniques of Artificial Life and the concerns of Artificial Intelligence is key (we feel) to enhancing the recognition that embodiment, developmental processes, and evolutionary processes all have a role to play in the emergence of natural intelligence – to overlook this whilst striving for artificial general intelligence is likely problematic. Simon Penny, an artist long engaged in Artificial Life art and robotics, provides for us a critical review of a new book by Sofian Audry, Art in the Age of Machine Learning (MIT Press 2021). The title might seem to be slightly out of line with Artificial Life’s main focus, perhaps even more suited to an AI readership, but, as Penny points out, this isn’t necessarily the case. In fact, by presenting both the practical artistic-technological concerns of the day, and the philosophical issues these raise with respect to agency, creativity and art-making by machines, Audry is in fact delving into areas that should concern us as researchers of Artificial Life. A topic infrequently explored within the pages of this journal is the impact that Artificial Life has on human relationships. In Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots, Paolo Euron enters this space by examining “physical beauty according to the artistic, cultural, and philosophical traditions”, of sexbots. Since Euron focuses on the visual appearance of these humanoid robots, with this article we have adopted a new approach for the Artificial Life journal to widen the perspective. The text is therefore supported by commentaries the editors have sought from alternative points of view. Thomas Arnold provides comment on Euron’s work from the perspective of Human-Robot Interaction by assessing the ethics of sex robots and how concepts of human trust, dignity, and autonomy potentially influence our interactions with such machines. Maria O’Sullivan examines how human interactions with sexbots relate to gender power relations and our expectations and human norms of intimacy and vulnerability. She also considers the very real dangers now widely associated with the commodification of beauty and the potential for moral h
{"title":"Editorial Introduction for 28:1","authors":"Alan Dorin;Susan Stepney","doi":"10.1162/artl_e_00378","DOIUrl":"10.1162/artl_e_00378","url":null,"abstract":"In this issue we are pleased to share with you a diverse set of reading materials. Sadly, we mark with an obituary the passing of Julian Miller, a researcher whose loss has been keenly felt within the community of Artificial Life researchers. He shall be sorely missed. On a much brighter note, the second installment of Chris Adami’s column exploring how artificial evolution might facilitate the design of General Intelligence is to be found within the pages of this issue. Adami explains how the indirect encoding of artificial brains to facilitate neuro-evolution might be managed. He discusses approaches to choosing an appropriate neuron, how to connect neurons to create a functioning network, how to train the network, and how the different options scale up to high levels of complexity. Drawing such connections between the techniques of Artificial Life and the concerns of Artificial Intelligence is key (we feel) to enhancing the recognition that embodiment, developmental processes, and evolutionary processes all have a role to play in the emergence of natural intelligence – to overlook this whilst striving for artificial general intelligence is likely problematic. Simon Penny, an artist long engaged in Artificial Life art and robotics, provides for us a critical review of a new book by Sofian Audry, Art in the Age of Machine Learning (MIT Press 2021). The title might seem to be slightly out of line with Artificial Life’s main focus, perhaps even more suited to an AI readership, but, as Penny points out, this isn’t necessarily the case. In fact, by presenting both the practical artistic-technological concerns of the day, and the philosophical issues these raise with respect to agency, creativity and art-making by machines, Audry is in fact delving into areas that should concern us as researchers of Artificial Life. A topic infrequently explored within the pages of this journal is the impact that Artificial Life has on human relationships. In Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots, Paolo Euron enters this space by examining “physical beauty according to the artistic, cultural, and philosophical traditions”, of sexbots. Since Euron focuses on the visual appearance of these humanoid robots, with this article we have adopted a new approach for the Artificial Life journal to widen the perspective. The text is therefore supported by commentaries the editors have sought from alternative points of view. Thomas Arnold provides comment on Euron’s work from the perspective of Human-Robot Interaction by assessing the ethics of sex robots and how concepts of human trust, dignity, and autonomy potentially influence our interactions with such machines. Maria O’Sullivan examines how human interactions with sexbots relate to gender power relations and our expectations and human norms of intimacy and vulnerability. She also considers the very real dangers now widely associated with the commodification of beauty and the potential for moral h","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42697751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy—evolution overarching development—is observed for most multicellular life forms. When designing robots, however, this tenet lifts: It becomes—however natural—a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots’ morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger, and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and it can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics.
{"title":"Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution","authors":"Fabien C. Y. Benureau;Jun Tani","doi":"10.1162/artl_a_00357","DOIUrl":"10.1162/artl_a_00357","url":null,"abstract":"Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy—evolution overarching development—is observed for most multicellular life forms. When designing robots, however, this tenet lifts: It becomes—however natural—a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots’ morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger, and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and it can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6720217/9930987/09934835.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48127124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The modern economy is both a complex self-organizing system and an innovative, evolving one. Contemporary theory, however, treats it essentially as a static equilibrium system. Here we propose a formal framework to capture its complex, evolving nature. We develop an agent-based model of an economic system in which firms interact with each other and with consumers through market transactions. Production functions are represented by a pair of von Neumann technology matrices, and firms implement production plans taking into account current price levels for their inputs and output. Prices are determined by the relation between aggregate demand and supply. In the absence of exogenous perturbations the system fluctuates around its equilibrium state. New firms are introduced when profits are above normal, and are ultimately eliminated when losses persist. The varying number of firms represents a recurrent perturbation. The system thus exhibits dynamics at two levels: the dynamics of prices and output, and the dynamics of system size. The model aims to be realistic in its fundamental structure, but is kept simple in order to be computationally efficient. The ultimate aim is to use it as a platform for modeling the structural evolution of an economic system. Currently the model includes one form of structural evolution, the ability to generate new technologies and new products.
{"title":"From Dynamics to Novelty: An Agent-Based Model of the Economic System","authors":"Gustavo Recio;Wolfgang Banzhaf;Roger White","doi":"10.1162/artl_a_00365","DOIUrl":"10.1162/artl_a_00365","url":null,"abstract":"The modern economy is both a complex self-organizing system and an innovative, evolving one. Contemporary theory, however, treats it essentially as a static equilibrium system. Here we propose a formal framework to capture its complex, evolving nature. We develop an agent-based model of an economic system in which firms interact with each other and with consumers through market transactions. Production functions are represented by a pair of von Neumann technology matrices, and firms implement production plans taking into account current price levels for their inputs and output. Prices are determined by the relation between aggregate demand and supply. In the absence of exogenous perturbations the system fluctuates around its equilibrium state. New firms are introduced when profits are above normal, and are ultimately eliminated when losses persist. The varying number of firms represents a recurrent perturbation. The system thus exhibits dynamics at two levels: the dynamics of prices and output, and the dynamics of system size. The model aims to be realistic in its fundamental structure, but is kept simple in order to be computationally efficient. The ultimate aim is to use it as a platform for modeling the structural evolution of an economic system. Currently the model includes one form of structural evolution, the ability to generate new technologies and new products.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43667718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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