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  • 1. Adami, C.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Erratum: Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything (Nature Communications (2013) 4:2193 DOI: 10.1038/ncomms3193)2014Ingår i: Nature Communications, E-ISSN 2041-1723, Vol. 5, artikel-id 3764Artikel i tidskrift (Refereegranskat)
  • 2. Adami, C.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything2013Ingår i: Nature Communications, E-ISSN 2041-1723, Vol. 4, artikel-id 2193Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Zero-determinant strategies are a new class of probabilistic and conditional strategies that are able to unilaterally set the expected payoff of an opponent in iterated plays of the Prisoner's Dilemma irrespective of the opponent's strategy (coercive strategies), or else to set the ratio between the player's and their opponent's expected payoff (extortionate strategies). Here we show that zero-determinant strategies are at most weakly dominant, are not evolutionarily stable, and will instead evolve into less coercive strategies. We show that zero-determinant strategies with an informational advantage over other players that allows them to recognize each other can be evolutionarily stable (and able to exploit other players). However, such an advantage is bound to be short-lived as opposing strategies evolve to counteract the recognition. © 2013 Macmillan Publishers Limited. All rights reserved.

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  • 3. Adami, C.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Thermodynamics of evolutionary games2018Ingår i: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 97, nr 6, artikel-id 062136Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    How cooperation can evolve between players is an unsolved problem of biology. Here we use Hamiltonian dynamics of models of the Ising type to describe populations of cooperating and defecting players to show that the equilibrium fraction of cooperators is given by the expectation value of a thermal observable akin to a magnetization. We apply the formalism to the public goods game with three players and show that a phase transition between cooperation and defection occurs that is equivalent to a transition in one-dimensional Ising crystals with long-range interactions. We then investigate the effect of punishment on cooperation and find that punishment plays the role of a magnetic field that leads to an "alignment" between players, thus encouraging cooperation. We suggest that a thermal Hamiltonian picture of the evolution of cooperation can generate other insights about the dynamics of evolving groups by mining the rich literature of critical dynamics in low-dimensional spin systems. © 2018 American Physical Society.

  • 4. Adami, C.
    et al.
    Qian, J.
    Rupp, M.
    Hintze, Arend
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States; Michigan State University, East Lansing, United States.
    Information content of colored motifs in complex networks2011Ingår i: Artificial Life, ISSN 1064-5462, E-ISSN 1530-9185, Vol. 17, nr 4, s. 375-390Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We study complex networks in which the nodes are tagged with different colors depending on their function (colored graphs), using information theory applied to the distribution of motifs in such networks. We find that colored motifs can be viewed as the building blocks of the networks (much more than the uncolored structural motifs can be) and that the relative frequency with which these motifs appear in the network can be used to define its information content. This information is defined in such a way that a network with random coloration (but keeping the relative number of nodes with different colors the same) has zero color information content. Thus, colored motif information captures the exceptionality of coloring in the motifs that is maintained via selection. We study the motif information content of the C. elegans brain as well as the evolution of colored motif information in networks that reflect the interaction between instructions in genomes of digital life organisms. While we find that colored motif information appears to capture essential functionality in the C. elegans brain (where the color assignment of nodes is straightforward), it is not obvious whether the colored motif information content always increases during evolution, as would be expected from a measure that captures network complexity. For a single choice of color assignment of instructions in the digital life form Avida, we find rather that colored motif information content increases or decreases during evolution, depending on how the genomes are organized, and therefore could be an interesting tool to dissect genomic rearrangements. © 2011 Massachusetts Institute of Technology.

  • 5. Adami, C.
    et al.
    Schossau, J.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Evolution and stability of altruist strategies in microbial games2012Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 85, nr 1, artikel-id 011914Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    When microbes compete for limited resources, they often engage in chemical warfare using bacterial toxins. This competition can be understood in terms of evolutionary game theory (EGT). We study the predictions of EGT for the bacterial "suicide bomber" game in terms of the phase portraits of population dynamics, for parameter combinations that cover all interesting games for two-players, and seven of the 38 possible phase portraits of the three-player game. We compare these predictions to simulations of these competitions in finite well-mixed populations, but also allowing for probabilistic rather than pure strategies, as well as Darwinian adaptation over tens of thousands of generations. We find that Darwinian evolution of probabilistic strategies stabilizes games of the rock-paper-scissors type that emerge for parameters describing realistic bacterial populations, and point to ways in which the population fixed point can be selected by changing those parameters. © 2012 American Physical Society.

  • 6. Adami, C.
    et al.
    Schossau, J.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Evolutionary game theory using agent-based methods2016Ingår i: Physics of Life Reviews, ISSN 1571-0645, E-ISSN 1873-1457, Vol. 19, s. 1-26Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection–strong mutation limit), but that mathematics is crucial to validate the computational simulations. © 2016 Elsevier B.V.

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  • 7. Adami, C.
    et al.
    Schossau, J.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    The reasonable effectiveness of agent-based simulations in evolutionary game theory: Reply to comments on “Evolutionary game theory using agent-based methods”2016Ingår i: Physics of Life Reviews, ISSN 1571-0645, E-ISSN 1873-1457, Vol. 19, s. 38-42Artikel i tidskrift (Refereegranskat)
  • 8. Albantakis, L.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Koch, C.
    Adami, C.
    Tononi, G.
    Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity2014Ingår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, nr 12Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment's causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ‘Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity. © 2014 Albantakis et al.

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  • 9. Bohm, C.
    et al.
    Ackles, A. L.
    Ofria, C.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    On sexual selection in the presence of multiple costly displays2020Ingår i: Proceedings of the 2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019, MIT Press , 2020, s. 247-254Konferensbidrag (Refereegranskat)
    Abstract [en]

    Sexual selection is a powerful yet poorly understood evolutionary force. Research into sexual selection, whether biological, computational, or mathematical, has tended to take a top-down approach studying complex natural systems. Many simplifying assumptions must be made in order to make these systems tractable, but it is unclear if these simplifications result in a system which still represents natural ecological and evolutionary dynamics. Here, we take a bottom-up approach in which we construct simple computational systems from subsets of biologically plausible components and focus on examining the underlying dynamics resulting from the interactions of those components. We use this method to investigate sexual selection in general and the sexy sons theory in particular. The minimally necessary components are therefore genomes, genome-determined displays and preferences, and a process capable of overseeing parent selection and mating. We demonstrate the efficacy of our approach (i.e we observe the evolution of female preference) and provide support for sexy sons theory, including illustrating the oscillatory behavior that developed in the presence of multiple costly display traits. Copyright © ALIFE 2019.All rights reserved.

  • 10. Bohm, Clifford
    et al.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Michigan State University, USA.
    Schossau, Jory
    A Simple Sparsity Function to Promote Evolutionary Search2023Ingår i: ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference, 2023, s. 368-376Konferensbidrag (Refereegranskat)
  • 11.
    Bohm, Clifford
    et al.
    Michigan State University, Department of Integrative Biology and BEACON Center for the Study of Evolution in Action, East Lansing, U.S.A..
    Kirkpatrick, Douglas
    Michigan State University, BEACON Center for the Study of Evolution in Action and Department of Computer Science and Engineering, East Lansing, U.S.A.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Michigan State University, BEACON Center for the Study of Evolution in Action, East Lansing, U.S.A..
    Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures.2022Ingår i: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 34, nr 3, s. 754-780Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.

  • 12. Edlund, J. A.
    et al.
    Chaumont, N.
    Hintze, Arend
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States; Michigan State University, East Lansing, United States.
    Koch, C.
    Tononi, G.
    Adami, C.
    Integrated information increases with fitness in the evolution of animats2011Ingår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 7, nr 10, artikel-id e1002236Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data. © 2011 Edlund et al.

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  • 13. Goldsby, H. J.
    et al.
    Young, R. L.
    Schossau, J.
    Hofmann, H. A.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Serendipitous scaffolding to improve a genetic algorithm's speed and quality2018Ingår i: GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc , 2018, s. 959-966Konferensbidrag (Refereegranskat)
    Abstract [en]

    A central challenge to evolutionary computation is enabling techniques to evolve increasingly complex target end products. Frequently, direct approaches that reward only the target end product itself are not successful because the path between the starting conditions and the target end product traverses through a complex fitness landscape, where the directly accessible intermediary states may be require deleterious or even simply neutral mutations. As such, a host of techniques have sprung up to support evolutionary computation techniques taking these paths. One technique is scaffolding where intermediary targets are used to provide a path from the starting state to the end state. While scaffolding can be successful within well-understood domains it also poses the challenge of identifying useful intermediaries. Within this paper we first identify some shortcomings of scaffolding approaches ' namely, that poorly selected intermediaries may in fact hurt the evolutionary computation's chance of producing the desired target end product. We then describe a light-weight approach to selecting intermediate scaffolding states that improve the efficacy of the evolutionary computation. © 2018 Association for Computing Machinery.

  • 14. Halabi, Ramzi
    et al.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Ortiz, Abigail
    Multi-resolution Time-frequency Spectral Derivative Spike Detection for Episode Onset Detection using Passively Collected Sensor Data2023Manuskript (preprint) (Övrigt vetenskapligt)
  • 15.
    Halabi, Ramzi
    et al.
    Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada, CA.
    Mulsant, Benoit H
    Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada, CA; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada, CA.
    Alda, Martin
    Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada, CA; National Institute of Mental Health, Klecany, Czech Republic, CZ.
    DeShaw, Alexandra
    Nova Scotia Health Authority, Halifax, Nova Scotia, Canada, CA.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Husain, Muhammad I
    Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada, CA; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada, CA.
    O'Donovan, Claire
    Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada, CA.
    Patterson, Rachel
    Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada, CA.
    Ortiz, Abigail
    Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada, CA; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada, CA.
    Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder2024Ingår i: Journal of Psychiatric Research, ISSN 0022-3956, E-ISSN 1879-1379, Vol. 174, s. 326-331, artikel-id S0022-3956(24)00242-5Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There is limited information on the association between participants' clinical status or trajectories and missing data in electronic monitoring studies of bipolar disorder (BD). We collected self-ratings scales and sensor data in 145 adults with BD. Using a new metric, Missing Data Ratio (MDR), we assessed missing self-rating data and sensor data monitoring activity and sleep. Missing data were lowest for participants in the midst of a depressive episode, intermediate for participants with subsyndromal symptoms, and highest for participants who were euthymic. Over a mean ± SD follow-up of 246 ± 181 days, missing data remained unchanged for participants whose clinical status did not change throughout the study (i.e., those who entered the study in a depressive episode and did not improve, or those who entered the study euthymic and remained euthymic). Conversely, when participants' clinical status changed during the study (e.g., those who entered the study euthymic and experienced the occurrence of a depressive episode), missing data for self-rating scales increased, but not for sensor data. Overall missing data were associated with participants' clinical status and its changes, suggesting that these are not missing at random.

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  • 16.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. BEACON Center for the Study of Evolution in Action, Michigan State University, USA .
    ChatGPT believes it is conscious2023Manuskript (preprint) (Övrigt vetenskapligt)
  • 17. Hintze, Arend
    Open-endedness for the sake of open-endedness2019Ingår i: Artificial Life, ISSN 1064-5462, E-ISSN 1530-9185, Vol. 25, nr 2, s. 198-206Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Natural evolution keeps inventing new complex and intricate forms and behaviors. Digital evolution and genetic algorithms fail to create the same kind of complexity, not just because we still lack the computational resources to rival nature, but because (it has been argued) we have not understood in principle how to create open-ended evolving systems. Much effort has been made to define such open-endedness so as to create forms of increasing complexity indefinitely. Here, however, a simple evolving computational system that satisfies all such requirements is presented. Doing so reveals a shortcoming in the definitions for open-ended evolution. The goal to create models that rival biological complexity remains. This work suggests that our current definitions allow for even simple models to pass as open-ended, and that our definitions of complexity and diversity are more important for the quest of open-ended evolution than the fact that something runs indefinitely. © 2019 Massachusetts Institute of Technology.

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  • 18.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    The Role Weights Play in Catastrophic Forgetting2021Ingår i: 2021 8TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE (ISCMI 2021), IEEE, 2021, s. 160-166Konferensbidrag (Refereegranskat)
    Abstract [en]

    Catastrophic forgetting is the sudden loss of performance when a neural network is trained on a new task or when experiencing unbalanced training. It often limits the ability of neural networks to learn new tasks. Previous work focused on the training data by changing the training regime, balancing the data, or replaying previous training episodes. Other methods used selective training to either allocate portions of the network to individual tasks or otherwise preserve prior task expertise. However, those approaches assume that network attractors are finely tuned, and even small changes to the weights cause misclassification. This fine-tuning is also believed to happen during overfitting and can be addressed with regularization. This paper introduces a method that quantifies how individual weights contribute to different tasks independent of weight strengths or previous training gradients. Applying this method reveals that backpropagation recruits all weights to contribute to a new task and that single weights may be somewhat more robust to noise than previously assumed.

  • 19.
    Hintze, Arend
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys. Michigan State University, East Lansing, United States.
    Adami, C.
    Cryptic Information Transfer in Differently-Trained Recurrent Neural Networks2020Ingår i: 2020 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020, 2020, s. 115-120Konferensbidrag (Refereegranskat)
  • 20.
    Hintze, Arend
    et al.
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States.
    Adami, C.
    Darwinian evolution of cooperation via punishment in the "public goods" game2010Ingår i: Artificial Life XII: Proceedings of the 12th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2010, 2010, s. 445-450Konferensbidrag (Refereegranskat)
    Abstract [en]

    The evolution of cooperation has been a perennial problem for evolutionary biology because cooperation is undermined by selfish cheaters (or "free riders") that profit from cooper-ators but do not invest any resources themselves. In a purely "selfish" view of evolution, those cheaters should be favored. Evolutionary game theory has been able to show that under certain conditions, cooperation nonetheless evolves stably. One of these scenarios utilizes the power of punishment to suppress free riders, but only if players interact in a structured population where cooperators are likely to be surrounded by other cooperators. Here we show that cooperation via punishment can evolve even in well-mixed populations that play the "public goods" game, if the synergy effect of cooperation is high enough. As the synergy is increased, populations transition from defection to cooperation in a manner reminiscent of a phase transition. If punishment is turned off the critical synergy is significantly higher, illustrating that indeed punishment aids in establishing cooperation. We also show that the critical point depends on the mutation rate so that higher mutation rates actually promote cooperation, by ensuring that punishment never disappears.

  • 21.
    Hintze, Arend
    et al.
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States.
    Adami, C.
    Evolution of complex modular biological networks2008Ingår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 4, nr 2Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Biological networks have evolved to be highly functional within uncertain environments while remaining extremely adaptable. One of the main contributors to the robustness and evolvability of biological networks is believed to be their modularity of function, with modules defined as sets of genes that are strongly interconnected but whose function is separable from those of other modules. Here, we investigate the in silico evolution of modularity and robustness in complex artificial metabolic networks that encode an increasing amount of information about their environment while acquiring ubiquitous features of biological, social, and engineering networks, such as scale-free edge distribution, small-world property, and fault-tolerance. These networks evolve in environments that differ in their predictability, and allow us to study modularity from topological, information-theoretic, and gene-epistatic points of view using new tools that do not depend on any preconceived notion of modularity. We find that for our evolved complex networks as well as for the yeast protein-protein interaction network, synthetic lethal gene pairs consist mostly of redundant genes that lie close to each other and therefore within modules, while knockdown suppressor gene pairs are farther apart and often straddle modules, suggesting that knockdown rescue is mediated by alternative pathways or modules. The combination of network modularity tools together with genetic interaction data constitutes a powerful approach to study and dissect the role of modularity in the evolution and function of biological networks. © 2008 Hintze and Adami.

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  • 22.
    Hintze, Arend
    et al.
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States.
    Adami, C.
    Modularity and anti-modularity in networks with arbitrary degree distribution2010Ingår i: Biology Direct, E-ISSN 1745-6150, Vol. 5, artikel-id 32Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Much work in systems biology, but also in the analysis of social network and communication and transport infrastructure, involves an in-depth analysis of local and global properties of those networks, and how these properties relate to the function of the network within the integrated system. Most often, systematic controls for such networks are difficult to obtain, because the features of the network under study are thought to be germane to that function. In most such cases, a surrogate network that carries any or all of the features under consideration, while created artificially and in the absence of any selective pressure relating to the function of the network being studied, would be of considerable interest.Results: Here, we present an algorithmic model for growing networks with a broad range of biologically and technologically relevant degree distributions using only a small set of parameters. Specifying network connectivity via an assortativity matrix allows us to grow networks with arbitrary degree distributions and arbitrary modularity. We show that the degree distribution is controlled mainly by the ratio of node to edge addition probabilities, and the probability for node duplication. We compare topological and functional modularity measures, study their dependence on the number and strength of modules, and introduce the concept of anti-modularity: a property of networks in which nodes from one functional group preferentially do not attach to other nodes of that group. We also investigate global properties of networks as a function of the network's growth parameters, such as smallest path length, correlation coefficient, small-world-ness, and the nature of the percolation phase transition. We search the space of networks for those that are most like some well-known biological examples, and analyze the biological significance of the parameters that gave rise to them.Conclusions: Growing networks with specified characters (degree distribution and modularity) provides the opportunity to create surrogates for biological and technological networks, and to test hypotheses about the processes that gave rise to them. We find that many celebrated network properties may be a consequence of the way in which these networks grew, rather than a necessary consequence of how they work or function.Reviewers: This article was reviewed by Erik van Nimwegen, Teresa Przytycka (nominated by Claus Wilke), and Leonid Mirny. For the full reviews, please go to the Reviewer's Comments section. © 2010 Hintze and Adami; licensee BioMed Central Ltd.

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  • 23.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Adami, C.
    Punishment in public goods games leads to meta-stable phase transitions and hysteresis2015Ingår i: Physical Biology, ISSN 1478-3967, E-ISSN 1478-3975, Vol. 12, nr 4, artikel-id 046005Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The evolution of cooperation has been a perennial problem in evolutionary biology because cooperation can be undermined by selfish cheaters who gain an advantage in the short run, while compromising the long-term viability of the population. Evolutionary game theory has shown that under certain conditions, cooperation nonetheless evolves stably, for example if players have the opportunity to punish cheaters that benefit from a public good yet refuse to pay into the common pool. However, punishment has remained enigmatic because it is costly and difficult to maintain. On the other hand, cooperation emerges naturally in the public goods game if the synergy of the public good (the factor multiplying the public good investment) is sufficiently high. In terms of this synergy parameter, the transition from defection to cooperation can be viewed as a phase transition with the synergy as the critical parameter. We show here that punishment reduces the critical value at which cooperation occurs, but also creates the possibility of meta-stable phase transitions, where populations can 'tunnel' into the cooperating phase below the critical value. At the same time, cooperating populations are unstable even above the critical value, because a group of defectors that are large enough can 'nucleate' such a transition. We study the mean-field theoretical predictions via agent-based simulations of finite populations using an evolutionary approach where the decisions to cooperate or to punish are encoded genetically in terms of evolvable probabilities. We recover the theoretical predictions and demonstrate that the population shows hysteresis, as expected in systems that exhibit super-heating and super-cooling. We conclude that punishment can stabilize populations of cooperators below the critical point, but it is a two-edged sword: it can also stabilize defectors above the critical point. © 2015 IOP Publishing Ltd.

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  • 24.
    Hintze, Arend
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Michigan State University, East Lansing, MI, USA.
    Adami, Christoph
    Michigan State University, East Lansing, MI, USA; .
    Detecting Information Relays in Deep Neural Networks2023Ingår i: Entropy, E-ISSN 1099-4300, Vol. 25, nr 3, artikel-id 401Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts. Identification of functional modules in natural brains plays an important role in cognitive and neuroscience alike, and can be carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or calcium imaging. However, we do not have such robust methods at our disposal when it comes to understanding functional modules in artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information IR. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to identify computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

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  • 25.
    Hintze, Arend
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Adami, Christoph
    Neuroevolution gives rise to more focused information transfer compared to backpropagation in recurrent neural networks2022Ingår i: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Artificial neural networks (ANNs) are one of the most promising tools in the quest to develop general artificial intelligence. Their design was inspired by how neurons in natural brains connect and process, the only other substrate to harbor intelligence. Compared to biological brains that are sparsely connected and that form sparsely distributed representations, ANNs instead process information by connecting all nodes of one layer to all nodes of the next. In addition, modern ANNs are trained with backpropagation, while their natural counterparts have been optimized by natural evolution over eons. We study whether the training method influences how information propagates through the brain by measuring the transfer entropy, that is, the information that is transferred from one group of neurons to another. We find that while the distribution of connection weights in optimized networks is largely unaffected by the training method, neuroevolution leads to networks in which information transfer is significantly more focused on small groups of neurons (compared to those trained by backpropagation) while also being more robust to perturbations of the weights. We conclude that the specific attributes of a training method (local vs. global) can significantly affect how information is processed and relayed through the brain, even when the overall performance is similar.

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  • 26.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Hertwig, R.
    The evolution of generosity in the ultimatum game2016Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 6, artikel-id 34102Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    When humans fail to make optimal decisions in strategic games and economic gambles, researchers typically try to explain why that behaviour is biased. To this end, they search for mechanisms that cause human behaviour to deviate from what seems to be the rational optimum. But perhaps human behaviour is not biased; perhaps research assumptions about the optimality of strategies are incomplete. In the one-shot anonymous symmetric ultimatum game (UG), humans fail to play optimally as defined by the Nash equilibrium. However, the distinction between kin and non-kin - with kin detection being a key evolutionary adaption - is often neglected when deriving the "optimal" strategy. We computationally evolved strategies in the UG that were equipped with an evolvable probability to discern kin from non-kin. When an opponent was not kin, agents evolved strategies that were similar to those used by humans. We therefore conclude that the strategy humans play is not irrational. The deviation between behaviour and the Nash equilibrium may rather be attributable to key evolutionary adaptations, such as kin detection. Our findings further suggest that social preference models are likely to capture mechanisms that permit people to play optimally in an evolutionary context. Once this context is taken into account, human behaviour no longer appears irrational © The Author(s) 2016.

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  • 27.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Kirkpatrick, D.
    Adami, C.
    The structure of evolved representations across different substrates for artificial intelligence2020Ingår i: ALIFE 2018 - 2018 Conference on Artificial Life: Beyond AI, MIT Press , 2020, s. 388-395Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of generalizing concepts, ANNs seem to focus on surface statistical regularities in a given task. Here we compare how recurrent artificial neural networks, long short-term memory units, and Markov Brains sense and remember their environments. We show that information in Markov Brains is localized and sparsely distributed, while the other neural network substrates “smear” information about the environment across all nodes, which makes them vulnerable to noise. Copyright © ALIFE 2018.All rights reserved.

  • 28.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Mirmomeni, M.
    Evolution of autonomous hierarchy formation and maintenance2014Ingår i: Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014, MIT Press Journals , 2014, s. 366-367Konferensbidrag (Refereegranskat)
    Abstract [en]

    Hierarchy among social animals is ubiquitous, and affects the social structures of gregarious species not only by interaction among species within the population, but also through other social forces such as mating, nesting location, amount and the quality of food they receive, or reproductive success. Since T. Schjelderup-Ebbe developed the structural definition of dominance and hierarchy in 1922 (see, e.g., Drews (1993)), different aspects of this social behavior have been addressed. However, exactly how hierarchies can emerge and be maintained among social species is still a conundrum. To investigate this issue, here we analyze a population of autonomous agents ('animates') through the course of evolution. The results of our experiments demonstrate the importance of memory and brain plasticity for the emergence of hierarchy and dominance behavior. © Artificial Life 14 - Proceedings of the 14th International Conference on the Synthesis and Simulation of Living Systems, ALIFE 2014. All rights reserved.

  • 29.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Olson, R. S.
    Adami, C.
    Hertwig, R.
    Risk sensitivity as an evolutionary adaptation2015Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 5, artikel-id 8242Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Risk aversion is a common behavior universal to humans and animals alike. Economists have traditionally defined risk preferences by the curvature of the utility function. Psychologists and behavioral economists also make use of concepts such as loss aversion and probability weighting to model risk aversion. Neurophysiological evidence suggests that loss aversion has its origins in relatively ancient neural circuitries (e.g., ventral striatum). Could there thus be an evolutionary origin to risk aversion? We study this question by evolving strategies that adapt to play the equivalent mean payoff gamble. We hypothesize that risk aversion in this gamble is beneficial as an adaptation to living in small groups, and find that a preference for risk averse strategies only evolves in small populations of less than 1,000 individuals, or in populations segmented into groups of 150 individuals or fewer - numbers thought to be comparable to what humans encountered in the past. We observe that risk aversion only evolves when the gamble is a rare event that has a large impact on the individual's fitness. As such, we suggest that rare, high-risk, high-payoff events such as mating and mate competition could have driven the evolution of risk averse behavior in humans living in small groups.

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  • 30.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Olson, R. S.
    Lehman, J.
    Orthogonally evolved AI to improve difficulty adjustment in video games2016Ingår i: Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science, vol 9597 / [ed] Squillero G., Burelli P., Springer Verlag , 2016, Vol. 9597, s. 525-540Konferensbidrag (Refereegranskat)
    Abstract [en]

    Computer games are most engaging when their difficulty is well matched to the player’s ability, thereby providing an experience in which the player is neither overwhelmed nor bored. In games where the player interacts with computer-controlled opponents, the difficulty of the game can be adjusted not only by changing the distribution of opponents or game resources, but also through modifying the skill of the opponents. Applying evolutionary algorithms to evolve the artificial intelligence that controls opponent agents is one established method for adjusting opponent difficulty. Less-evolved agents (i.e., agents subject to fewer generations of evolution) make for easier opponents, while highlyevolved agents are more challenging to overcome. In this publication we test a new approach for difficulty adjustment in games: orthogonally evolved AI, where the player receives support from collaborating agents that are co-evolved with opponent agents (where collaborators and opponents have orthogonal incentives). The advantage is that game difficulty can be adjusted more granularly by manipulating two independent axes: by having more or less adept collaborators, and by having more or less adept opponents. Furthermore, human interaction can modulate (and be informed by) the performance and behavior of collaborating agents. In this way, orthogonally evolved AI both facilitates smoother difficulty adjustment and enables new game experiences. © Springer International Publishing Switzerland 2016.

  • 31.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Phillips, N.
    Hertwig, R.
    The Janus face of Darwinian competition2015Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 5, artikel-id 13662Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Without competition, organisms would not evolve any meaningful physical or cognitive abilities. Competition can thus be understood as the driving force behind Darwinian evolution. But does this imply that more competitive environments necessarily evolve organisms with more sophisticated cognitive abilities than do less competitive environments? Or is there a tipping point at which competition does more harm than good? We examine the evolution of decision strategies among virtual agents performing a repetitive sampling task in three distinct environments. The environments differ in the degree to which the actions of a competitor can affect the fitness of the sampling agent, and in the variance of the sample. Under weak competition, agents evolve decision strategies that sample often and make accurate decisions, which not only improve their own fitness, but are good for the entire population. Under extreme competition, however, the dark side of the Janus face of Darwinian competition emerges: Agents are forced to sacrifice accuracy for speed and are prevented from sampling as often as higher variance in the environment would require. Modest competition is therefore a good driver for the evolution of cognitive abilities and of the population as a whole, whereas too much competition is devastating.

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  • 32.
    Hintze, Arend
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys. Michigan State University, East Lansing, USA.
    Staudacher, Jochen
    Gelhar, Katja
    Pothmann, Alexander
    Rasch, Juliana
    Wildegger, Daniel
    Inclusive groups can avoid the tragedy of the commons2020Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 10, nr 1, artikel-id 22392Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The public goods game is a famous example illustrating the tragedy of the commons (Hardin in Science 162:1243-1248, 1968). In this game cooperating individuals contribute to a pool, which in turn is distributed to all members of the group, including defectors who reap the same rewards as cooperators without having made a contribution before. The question is now, how to incentivize group members to all cooperate as it maximizes the common good. While costly punishment (Helbing et al. in New J Phys 12:083005, 2010) presents one such method, the cost of punishment still reduces the common good. The selfishness of the group members favors defectors. Here we show that including other members of the groups and sharing rewards with them can be another incentive for cooperation, avoiding the cost required for punishment. Further, we show how punishment and this form of inclusiveness interact. This work suggests that a redistribution similar to a basic income that is coupled to the economic success of the entire group could overcome the tragedy of the commons.

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  • 33. Iliopoulos, D.
    et al.
    Hintze, Arend
    Keck Graduate Institute of Applied Life Sciences, Claremont, United States.
    Adami, C.
    Critical dynamics in the evolution of stochastic strategies for the iterated Prisoner's Dilemma2010Ingår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, nr 10, artikel-id 1000948Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The observed cooperation on the level of genes, cells, tissues, and individuals has been the object of intense study by evolutionary biologists, mainly because cooperation often flourishes in biological systems in apparent contradiction to the selfish goal of survival inherent in Darwinian evolution. In order to resolve this paradox, evolutionary game theory has focused on the Prisoner's Dilemma (PD), which incorporates the essence of this conflict. Here, we encode strategies for the iterated Prisoner's Dilemma (IPD) in terms of conditional probabilities that represent the response of decision pathways given previous plays. We find that if these stochastic strategies are encoded as genes that undergo Darwinian evolution, the environmental conditions that the strategies are adapting to determine the fixed point of the evolutionary trajectory, which could be either cooperation or defection. A transition between cooperative and defective attractors occurs as a function of different parameters such as mutation rate, replacement rate, and memory, all of which affect a player's ability to predict an opponent's behavior. These results imply that in populations of players that can use previous decisions to plan future ones, cooperation depends critically on whether the players can rely on facing the same strategies that they have adapted to. Defection, on the other hand, is the optimal adaptive response in environments that change so quickly that the information gathered from previous plays cannot usefully be integrated for a response. © 2010 Iliopoulos et al.

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  • 34. Incorvaia, Darren C.
    et al.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Michigan State Univ.
    Dyer, Fred C.
    Spatial allocation without spatial recruitment in bumblebees2021Ingår i: Behavioral Ecology, ISSN 1045-2249, E-ISSN 1465-7279, Vol. 32, nr 2, s. 265-276Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Any foraging animal is expected to allocate its efforts among resource patches that vary in quality across time and space. For social insects, this problem is shifted to the colony level: the task of allocating foraging workers to the best patches currently available. To deal with this task, honeybees rely upon differential recruitment via the dance language, while some ants use differential recruitment on odor trails. Bumblebees, close relatives of honeybees, should also benefit from optimizing spatial allocation but lack any targeted recruitment system. How bumblebees solve this problem is thus of immense interest to evolutionary biologists studying collective behavior. It has been thought that bumblebees could solve the spatial allocation problem by relying on the summed individual decisions of foragers, who occasionally sample and shift to alternative resources. We use field experiments to test the hypothesis that bumblebees augment individual exploration with social information. Specifically, we provide behavioral evidence that, when higher-concentration sucrose arrives at the nest, employed foragers abandon their patches to begin searching for the better option; they are more likely to accept novel resources if they match the quality of the sucrose solution experienced in the nest. We explored this strategy further by building an agent-based model of bumblebee foraging. This model supports the hypothesis that using social information to inform search decisions is advantageous over individual search alone. Our results show that bumblebees use a collective foraging strategy built on social modulation of individual decisions, providing further insight into the evolution of collective behavior.

  • 35. Jack, C. N.
    et al.
    Friesen, M. L.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Sheneman, L.
    Third-party mutualists have contrasting effects on host invasion under the enemy-release and biotic-resistance hypotheses2017Ingår i: Evolutionary Ecology, ISSN 0269-7653, E-ISSN 1573-8477, Vol. 31, nr 6, s. 829-845Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Plants engage in complex multipartite interactions with mutualists and antagonists, but these interactions are rarely included in studies that explore plant invasiveness. When considered in isolation, we know that beneficial microbes can enhance an exotic plant’s invasive ability and that herbivorous insects often decrease an exotic plant’s likeliness of success. However, the effect of these partners on plant fitness has not been well characterized when all three species coevolve. We use computational evolutionary modeling of a trait-based system to test how microbes and herbivores simultaneously coevolving with an invading plant affect the invaders’ probability of becoming established. Specifically, we designed a model that explores how a beneficial microbe would influence the outcome of an interaction between a plant and herbivore. To model novel interactions, we included a phenotypic trait shared by each species. Making this trait continuous and selectable allows us to explore how trait similarities between coevolving plants, herbivores and microbes affect fitness. Using this model, we answer the following questions: (1) Can a beneficial plant-microbe interaction influence the evolutionary outcome of antagonistic interactions between plants and herbivores? (2) How does the initial trait similarity between interacting organisms affect the likelihood of plant survival in novel locations? (3) Does the effect of tripartite interactions on the invasion success of a plant depend on whether organisms interact through trait similarity [Enemy Release Hypothesis (ERH)] or dissimilarity (Biotic Resistance Hypothesis)? We found that it was much more difficult for plants to invade under the ERH but that beneficial microbes increase the probability of plant survival in a novel range under both hypotheses. To our knowledge, this model is the first to use tripartite interactions to explore novel species introductions. It represents a step towards gaining a better understanding of the factors influencing establishment of exotic species to prevent future invasions. © 2017, The Author(s).

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  • 36. Jahns, J.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    How the integration of group and individual level selection affects the evolution of cooperation2020Ingår i: ALIFE 2018 - 2018 Conference on Artificial Life: Beyond AI, MIT Press , 2020, s. 530-535Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many evolutionary models that explore the emergence of cooperation rely on either individual level selection or group level selection. However, natural systems are often more complex and selection is never just on the level of the individual or group alone. Here we explore how systems of collaborating agents evolve when selection is based on a mixture of group and individual performances. It has been suggested that under such situations free riders thrive and hamper evolution significantly. Here we show that free rider effects can almost be ignored. Sharing resources even with free riders benefits the evolution of cooperators, which in the long run is more beneficial than the short term cost. Copyright © ALIFE 2018.All rights reserved.

  • 37. Kirkpatrick, D.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Augmenting neuro-evolutionary adaptation with representations does not incur a speed accuracy trade-off2019Ingår i: GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc , 2019, s. 177-178Konferensbidrag (Refereegranskat)
    Abstract [en]

    Representations, or sensor-independent internal models of the environment, are important for any type of intelligent agent to process and act in an environment. Imbuing an artificially intelligent system with such a model of the world it functions in remains a difficult problem. However, using neuro-evolution as the means to optimize such a system allows the artificial intelligence to evolve proper models of the environment. Previous work has found an information-theoretic measure, R, which measures how much information a neural computational architecture (henceforth loosely referred to as a brain) has about its environment, and can additionally be used speed up the neuro-evolutionary process. However, it is possible that this improved evolutionary adaptation comes at a cost to the brain's ability to generalize or the brain's robustness to noise. In this paper, we show that this is not the case; to the contrary, we find an improved ability of the to evolve in noisy environments when the neuro-correlate R is used to augment evolutionary adaptation. © 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.

  • 38. Kirkpatrick, D.
    et al.
    Hintze, Arend
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Evolutionary Dynamics Effects Account for the Improvement Caused by R-Augmentation2020Ingår i: 2020 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020, 2020, s. 96-100, artikel-id 9311590Konferensbidrag (Refereegranskat)
  • 39. Kirkpatrick, D.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    The role of ambient noise in the evolution of robust mental representations in cognitive systems2020Ingår i: Proceedings of the 2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019, MIT Press , 2020, s. 432-439Konferensbidrag (Refereegranskat)
    Abstract [en]

    Natural environments are full of ambient noise; nevertheless, natural cognitive systems deal greatly with uncertainty but also have ways to suppress or ignore noise unrelated to the task at hand. For most intelligent tasks, experiences and observations have to be committed to memory and these representations of reality inform future decisions. We know that deep learned artificial neural networks (ANNs) often struggle with the formation of representations. This struggle may be due to the ANN's fully interconnected, layered architecture. This forces information to be propagated over the entire system, which is different from natural brains that instead have sparsely distributed representations. Here we show how ambient noise causes neural substrates such as recurrent ANNs and long short-term memory neural networks to evolve more representations in order to function in these noisy environments, which also greatly improves their functionality. However, these systems also tend to further smear their representations over their internal states making them more vulnerable to internal noise. We also show that Markov Brains (MBs) are mostly unaffected by ambient noise, and their representations remain sparsely distributed (i.e. not smeared). This suggests that ambient noise helps to increase the amount of representations formed in neural networks, but also requires us to find additional solutions to prevent smearing of said representations. Copyright © ALIFE 2019.All rights reserved.

  • 40. Kvam, P. D.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Rewards, risks, and reaching the right strategy: Evolutionary paths from heuristics to optimal decisions2018Ingår i: Evolutionary Behavioral Sciences, ISSN 2330-2925, E-ISSN 2330-2933, Vol. 12, nr 3, s. 177-190Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Theories of decision-making often posit optimal or heuristic strategies for performing a task. In optimal strategies, information is integrated over time in order to achieve the ideal outcomes; in the heuristic case, some shortcut or simplification is applied in order to make the decision faster or easier. In this article, we use a computational framework to study the evolution of both types of decision strategies in artificial agents. The fitness of these agents is assessed based on their performance on a sequential decision task where they must accurately identify the source of as many incoming information signals as they can over a finite time span. In order to examine what decision strategies evolve as a function of task characteristics, we manipulate the quality of decision information (difficulty) and the magnitude of punishments for incorrect answers. We find that trivial (but optimal) strategies evolve when punishment magnitude is lower than the reward magnitude for correct answers, and optimal information-integrating strategies evolve when either punishment magnitude is low or information quality is high. However, the computational demands of the task become much greater as information quality decreases and punishment magnitudes increase. In these cases, heuristics are used to maintain decision accuracy in spite of the limited cognitive resources agents have available. The results suggest that heuristics are an evolved response to environments with high demands on cognitive resources, where optimal strategies are particularly difficult to achieve. © 2018 American Psychological Association.

  • 41. Kvam, Peter D
    et al.
    Sokratous, Konstantina
    Fitch, Anderson
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Using artificial intelligence to fit, compare, evaluate, and discover models of decision behavior2023Manuskript (preprint) (Övrigt vetenskapligt)
  • 42. Labar, T.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Adami, C.
    Evolvability tradeoffs in emergent digital replicators2016Ingår i: Artificial Life, ISSN 1064-5462, E-ISSN 1530-9185, Vol. 22, nr 4, s. 483-498Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The role of historical contingency in the origin of life is one of the great unknowns in modern science. Only one example of life exists - one that proceeded from a single self-replicating organism (or a set of replicating hypercycles) to the vast complexity we see today in Earth's biosphere. We know that emergent life has the potential to evolve great increases in complexity, but it is unknown if evolvability is automatic given any self-replicating organism. At the same time, it is difficult to test such questions in biochemical systems. Laboratory studies with RNA replicators have had some success with exploring the capacities of simple self-replicators, but these experiments are still limited in both capabilities and scope. Here, we use the digital evolution system Avida to explore the interplay between emergent replicators (rare randomly assembled self-replicators) and evolvability. We find that we can classify fixed-length emergent replicators in Avida into two classes based on functional analysis. One class is more evolvable in the sense of optimizing the replicators' replication abilities. However, the other class is more evolvable in the sense of acquiring evolutionary innovations. We tie this tradeoff in evolvability to the structure of the respective classes' replication machinery, and speculate on the relevance of these results to biochemical replicators. © 2016 Massachusetts Institute of Technology.

  • 43. Marstaller, L.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Adami, C.
    The evolution of representation in simple cognitive networks2013Ingår i: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 25, nr 8, s. 2079-2107Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Representations are internalmodels of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether they are necessary or even essential for intelligent behavior.We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks-an artificial neural network and a network of hiddenMarkov gates-to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system and should be predictive of an agent's long-term adaptive success. © 2013 Massachusetts Institute of Technology.

  • 44.
    Mehra, Priyanka
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. BEACON Center for the Study of Evolution in Action Michigan State University East Lansing, USA.
    An extension to the NK fitness landscape model to study pleiotropy, epistasis, and ruggedness independently2022Ingår i: Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, s. 1259-1267Konferensbidrag (Refereegranskat)
    Abstract [en]

    The NK model is designed to study evolutionary adaptation in rugged fitness landscapes. The factor K determines the number of interacting genes, their degree of pleiotropy and epistasis, and consequently the ruggedness of the fitness landscape. However, in natural organisms, the degree of epistatic interactions and the number of functions a gene can have are to a certain degree determining the ruggedness of the landscape. Still, pleiotropy and epistasis can evolve independently from each other, and are to some degree independent of the ruggedness of the landscape. Here, we propose an extension to the standard NK model to investigate these factors independently of each other. Over the course of evolution the computational model organisms can now change how their genes interact and how they control phenotypic traits. Further, the degree of epistasis and pleiotropy is affected by the ruggedness of the landscape and becomes reduced with increasing ruggedness. While this proves that the extension of the model performs as expected, the adaptations are minor, presumably because only relatively short periods of adaptations with few mutations can be studied. © 2022 IEEE.

  • 45. Mehra, Priyanka
    et al.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Michigan State University, USA.
    Evolution of Pleiotropy and Epistasis in a Gene Regulatory Network2023Ingår i: ALIFE 2023. Ghost in The Machine. Proceedings of the Artificial Life Conference 2023, MIT Press, 2023, s. 685-687Konferensbidrag (Refereegranskat)
  • 46.
    Mehra, Priyanka
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, USA.
    Reducing Epistasis and Pleiotropy Can Avoid the Survival of the Flattest Tragedy2024Ingår i: Biology, E-ISSN 2079-7737, Vol. 13, nr 3, artikel-id 193Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study investigates whether reducing epistasis and pleiotropy enhances mutational robustness in evolutionary adaptation, utilizing an indirect encoded model within the “survival of the flattest” (SoF) fitness landscape. By simulating genetic variations and their phenotypic consequences, we explore organisms’ adaptive mechanisms to maintain positions on higher, narrower evolutionary peaks amidst environmental and genetic pressures. Our results reveal that organisms can indeed sustain their advantageous positions by minimizing the complexity of genetic interactions—specifically, by reducing the levels of epistasis and pleiotropy. This finding suggests a counterintuitive strategy for evolutionary stability: simpler genetic architectures, characterized by fewer gene interactions and multifunctional genes, confer a survival advantage by enhancing mutational robustness. This study contributes to our understanding of the genetic underpinnings of adaptability and robustness, challenging traditional views that equate complexity with fitness in dynamic environments. © 2024 by the authors.

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  • 47. Nitash, C. G.
    et al.
    LaBar, T.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Adami, C.
    Origin of life in a digital microcosm2017Ingår i: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 375, nr 2109, artikel-id 20160350Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    While all organisms on Earth share a common descent, there is no consensus on whether the origin of the ancestral self-replicator was a oneoff event or whether it only represented the final survivor of multiple origins. Here, we use the digital evolution system Avida to study the origin of self-replicating computer programs. By using a computational system, we avoid many of the uncertainties inherent in any biochemical system of self-replicators (while running the risk of ignoring a fundamental aspect of biochemistry). We generated the exhaustive set of minimal-genome self-replicators and analysed the network structure of this fitness landscape. We further examined the evolvability of these self-replicators and found that the evolvability of a self-replicator is dependent on its genomic architecture. We also studied the differential ability of replicators to take over the population when competed against each other, akin to a primordialsoup model of biogenesis, and found that the probability of a self-replicator outcompeting the others is not uniform. Instead, progenitor (mostrecent common ancestor) genotypes are clustered in a small region of the replicator space. Our results demonstrate how computational systems can be used as test systems for hypotheses concerning the origin of life. This article is part of the themed issue 'Reconceptualizing the origins of life'.

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  • 48. Nitash, C. G.
    et al.
    Lundrigan, B.
    Smale, L.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    The effect of periodic changes in the fitness landscape on brain structure and function2020Ingår i: ALIFE 2018 - 2018 Conference on Artificial Life: Beyond AI, MIT Press , 2020, s. 469-476Konferensbidrag (Refereegranskat)
    Abstract [en]

    Natural organisms have transitioned from one niche to another over the course of evolution and have adapted accordingly. In particular, if these transition go back and forth between two niches repeatedly, such as transitioning between diurnal and nocturnal lifestyles, this should over time result in adaptations that are beneficial to both environments. Furthermore, they should also adapt to the transitions themselves. Here we answer how Markov Brains, which are an analogue to natural brains, change structurally and functionally when experiencing periodic changes. We show that if environments change sufficiently fast, the structural components that form the brains become useful in both environments. However, brains evolve to perform different computations while using the same components, and thus have computational structures that are multifunctional. Copyright © ALIFE 2018.All rights reserved.

  • 49. Olson, R. S.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Dyer, F. C.
    Knoester, D. B.
    Adami, C.
    Predator confusion is sufficient to evolve swarming behaviour2013Ingår i: Journal of the Royal Society Interface, ISSN 1742-5689, E-ISSN 1742-5662, Vol. 10, nr 85Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Swarming behaviours in animals have been extensively studied owing to their implications for the evolution of cooperation, social cognition and predator-prey dynamics. An important goal of these studies is discerning which evolutionary pressures favour the formation of swarms. One hypothesis is that swarms arise because the presence of multiple moving prey in swarms causes confusion for attacking predators, but it remains unclear how important this selective force is. Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behaviour in prey. Furthermore, we demonstrate that the evolutionary effect of predator confusion on prey could in turn exert pressure on the structure of the predator's visual field, favouring the frontally oriented, high-resolution visual systems commonly observed in predators that feed on swarming animals. Finally, we provide evidence that when prey evolve swarming in response to predator confusion, there is a change in the shape of the functional response curve describing the predator's consumption rate as prey density increases. Thus, we show that a relatively simple perceptual constraint-predator confusion-could have pervasive evolutionary effects on prey behaviour, predator sensory mechanisms and the ecological interactions between predators and prey. © 2013 The Author(s) Published by the Royal Society. All rights reserved.

  • 50. Olson, R. S.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Dyer, F. C.
    Moore, J. H.
    Adami, C.
    Exploring the coevolution of predator and prey morphology and behavior2016Ingår i: Proceedings of the Artificial Life Conference 2016, ALIFE 2016, MIT Press Journals , 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    A common idiom in biology education states, “Eyes in the front, the animal hunts. Eyes on the side, the animal hides.” In this paper, we explore one possible explanation for why predators tend to have forward-facing, high-acuity visual systems. We do so using an agent-based computational model of evolution, where predators and prey interact and adapt their behavior and morphology to one another over successive generations of evolution. In this model, we observe a coevolutionary cycle between prey swarming behavior and the predator’s visual system, where the predator and prey continually adapt their visual system and behavior, respectively, over evolutionary time in reaction to one another due to the well-known “predator confusion effect.” Furthermore, we provide evidence that the predator visual system is what drives this coevolutionary cycle, and suggest that the cycle could be closed if the predator evolves a hybrid visual system capable of narrow, high-acuity vision for tracking prey as well as broad, coarse vision for prey discovery. Thus, the conflicting demands imposed on a predator’s visual system by the predator confusion effect could have led to the evolution of complex eyes in many predators. © 2016 MIT Press. All rights reserved.

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