Self-Reproduction and Evolution in Cellular Automata: 25 Years After Evoloops.

The year 2024 marks the 25th anniversary of the publication of evoloops, an evolutionary variant of Chris Langton's self-reproducing loops, which proved constructively that Darwinian evolution of self-reproducing organisms by variation and natural selection is possible within deterministic cellular automata. Over the last few decades, this line of Artificial Life research has since undergone several important developments. Although it experienced a relative dormancy of activity for a while, the recent rise of interest in open-ended evolution and the success of continuous cellular automata models have brought researchers' attention back to how to make spatiotemporal patterns self-reproduce and evolve within spatially distributed computational media. This article provides a review of the relevant literature on this topic over the past 25 years and highlights the major accomplishments made so far, the challenges being faced, and promising future research directions.

Network analysis of discrete emotional states measured via ecological momentary assessment in schizophrenia.

Prior studies demonstrate that schizophrenia (SZ) is associated with abnormalities in positive and negative emotional experience that predict clinical presentation. However, it is unclear whether specific discrete emotions within the broader positive/negative categories are driving those symptom associations. Further, it is also unclear whether specific emotions contribute to symptoms in isolation or via networks of emotional states that dynamically interact across time. The current study used network analysis to evaluate temporally dynamic interactions among discrete emotional states experienced in the real world as assessed via Ecological Momentary Assessment (EMA). Participants included 46 outpatients with chronic SZ and 52 demographically matched healthy controls (CN) who completed 6 days of EMA that captured reports of emotional experience and symptoms derived from monetary surveys and geolocation based symptom markers of mobility and home location. Results indicated that less dense emotion networks were associated with greater severity of negative symptoms, whereas more dense emotion networks were associated with more severe positive symptoms and mania. Additionally, SZ evidenced greater centrality for shame, which was associated with greater severity of positive symptoms. These findings suggest that positive and negative symptoms are associated with distinct profiles of temporally dynamic and interactive emotion networks in SZ. Findings have implications for adapting psychosocial therapies to target specific discrete emotional states in the treatment of positive versus negative symptoms.

How Lévy Flights Triggered by the Presence of Defectors Affect Evolution of Cooperation in Spatial Games.

Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a Lévy flight. A Lévy flight is a kind of random walk but it is composed of many small movements with a few big movements. The role of Lévy flights for cooperation has been studied by Antonioni and Tomassini, who showed that Lévy flights promoted cooperation combined with conditional movements triggered by neighboring defectors. However, the optimal condition for neighboring defectors and how the condition changes with the intensity of Lévy flights are still unclear. Here, we developed an agent-based model in a square lattice where agents perform Lévy flights depending on the fraction of neighboring defectors. We systematically studied the relationships among three factors for cooperation: sensitivity to defectors, the intensity of Lévy flights, and population density. Results of evolutionary simulations showed that moderate sensitivity most promoted cooperation. Then, we found that the shortest movements were best for cooperation when the sensitivity to defectors was high. In contrast, when the sensitivity was low, longer movements were best for cooperation. Thus, Lévy flights, the balance between short and long jumps, promoted cooperation in any sensitivity, which was confirmed by evolutionary simulations. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve. Our study highlights that Lévy flights are an optimal searching strategy not only for foraging but also for constructing cooperative relationships with others.

Health information exchange network under collaboration, cooperation, and competition: A game-theoretic approach.

Health Information Exchange (HIE) network allows securely accessing and sharing healthcare-related information among healthcare providers (HCPs) and payers. HIE services are provided by a non-profit/profit organizations under several subscription plans options. A few studies have addressed the sustainability of the HIE network such that HIE providers, HCPs, and payers remain profitable in the long term. However, none of these studies addressed the coexistence of multiple HIE providers in the network. Such coexistence may have a huge impact on the behavior of healthcare systems in terms of adoption rate and HIE pricing strategies. In addition, in spite of all the effort to maintain cooperation between HIE providers, there is still a chance of competition among them in the market. Possible competition among service providers leads to many concerns about the HIE network sustainability and behavior. In this study, a game-theoretic approach to model the HIE market is proposed. Game-theory is used to simulate the behavior of the three different HIE network agents in the HIE market: HIE providers, HCPs, and payers. Pricing strategies and adoption decisions are optimized using a Linear Programming (LP) mathematical model. Results show that the relation between HIEs in the market is crucial to HCP/Payer adoption decision specially to small HCPs. A small change in the discount rate proposed by a competitive HIE provider will highly affect the decision of HCP/payers to join the HIE network. Finally, competition opened the opportunity for more HCPs to join the network due to reduced pricing. Furthermore, collaborative HIEs provided better performance compared to cooperative in terms of profit and HCP adoption rate by sharing their overall costs and revenues.

Behaviour and the Origin of Organisms.

It is common in origins of life research to view the first stages of life as the passive result of particular environmental conditions. This paper considers the alternative possibility: that the antecedents of life were already actively regulating their environment to maintain the conditions necessary for their own persistence. In support of this proposal, we describe 'viability-based behaviour': a way that simple entities can adaptively regulate their environment in response to their health, and in so doing, increase the likelihood of their survival. Drawing on empirical investigations of simple self-preserving abiological systems, we argue that these viability-based behaviours are simple enough to precede neo-Darwinian evolution. We also explain how their operation can reduce the demanding requirements that mainstream theories place upon the environment(s) in which life emerged.

Multilayer networks with higher-order interaction reveal the impact of collective behavior on epidemic dynamics.

The ongoing COVID-19 pandemic has inflicted tremendous economic and societal losses. In the absence of pharmaceutical interventions, the population behavioral response, including situational awareness and adherence to non-pharmaceutical intervention policies, has a significant impact on contagion dynamics. Game-theoretic models have been used to reproduce the concurrent evolution of behavioral responses and disease contagion, and social networks are critical platforms on which behavior imitation between social contacts, even dispersed in distant communities, takes place. Such joint contagion dynamics has not been sufficiently explored, which poses a challenge for policies aimed at containing the infection. In this study, we present a multi-layer network model to study contagion dynamics and behavioral adaptation. It comprises two physical layers that mimic the two solitary communities, and one social layer that encapsulates the social influence of agents from these two communities. Moreover, we adopt high-order interactions in the form of simplicial complexes on the social influence layer to delineate the behavior imitation of individual agents. This model offers a novel platform to articulate the interaction between physically isolated communities and the ensuing coevolution of behavioral change and spreading dynamics. The analytical insights harnessed therefrom provide compelling guidelines on coordinated policy design to enhance the preparedness for future pandemics.

Life Worth Mentioning: Complexity in Life-Like Cellular Automata.

Cellular automata (CA) have been lauded for their ability to generate complex global patterns from simple local rules. The late English mathematician, John Horton Conway, developed his illustrious Game of Life (Life) CA in 1970, which has since remained one of the most quintessential CA constructions-capable of producing a myriad of complex dynamic patterns and computational universality. Life and several other Life-like rules have been classified in the same group of aesthetically and dynamically interesting CA rules characterized by their complex behaviors. However, a rigorous quantitative comparison among similarly classified Life-like rules has not yet been fully established. Here we show that Life is capable of maintaining as much complexity as similar rules while remaining the most parsimonious. In other words, Life contains a consistent amount of complexity throughout its evolution, with the least number of rule conditions compared to other Life-like rules. We also found that the complexity of higher density Life-like rules, which themselves contain the Life rule as a subset, form a distinct concave density-complexity relationship whereby an optimal complexity candidate is proposed. Our results also support the notion that Life functions as the basic ingredient for cultivating the balance between structure and randomness to maintain complexity in 2D CA for low- and high-density regimes, especially over many iterations. This work highlights the genius of John Horton Conway and serves as a testament to his timeless marvel, which is referred to simply as: Life.

Network-Based Phase Space Analysis of the El Farol Bar Problem.

The El Farol Bar problem highlights the issue of bounded rationality through a coordination problem where agents must decide individually whether or not to attend a bar without prior communication. Each agent is provided a set of attendance predictors (or decision-making strategies) and uses the previous bar attendances to guess bar attendance for a given week to determine if the bar is worth attending. We previously showed how the distribution of used strategies among the population settles into an attractor by using a spatial phase space. However, this approach was limited as it required N - 1 dimensions to fully visualize the phase space of the problem, where N is the number of strategies available. Here we propose a new approach to phase space visualization and analysis by converting the strategy dynamics into a state transition network centered on strategy distributions. The resulting weighted, directed network gives a clearer representation of the strategy dynamics once we define an attractor of the strategy phase space as a sink-strongly connected component. This enables us to study the resulting network to draw conclusions about the performance of the different strategies. We find that this approach not only is applicable to the El Farol Bar problem, but also addresses the dimensionality issue and is theoretically applicable to a wide variety of discretized complex systems.

Efficient sentinel surveillance strategies for preventing epidemics on networks.

Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data.

Self-Organization and Artificial Life.

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

Cardinality Leap for Open-Ended Evolution: Theoretical Consideration and Demonstration by Hash Chemistry.

Open-ended evolution requires unbounded possibilities that evolving entities can explore. The cardinality of a set of those possibilities thus has a significant implication for the open-endedness of evolution. I propose that facilitating formation of higher-order entities is a generalizable, effective way to cause a cardinality leap in the set of possibilities that promotes open-endedness. I demonstrate this idea with a simple, proof-of-concept toy model called Hash Chemistry that uses a hash function as a fitness evaluator of evolving entities of any size or order. Simulation results showed that the cumulative number of unique replicating entities that appeared in evolution increased almost linearly along time without an apparent bound, demonstrating the effectiveness of the proposed cardinality leap. It was also observed that the number of individual entities involved in a single replication event gradually increased over time, indicating evolutionary appearance of higher-order entities. Moreover, these behaviors were not observed in control experiments in which fitness evaluators were replaced by random number generators. This strongly suggests that the dynamics observed in Hash Chemistry were indeed evolutionary behaviors driven by selection and adaptation taking place at multiple scales.

Evolved Open-Endedness, Not Open-Ended Evolution.

Open-endedness is often considered a prerequisite property of the whole evolutionary system and its dynamical behaviors. In the actual history of evolution on Earth, however, there are many examples showing that open-endedness is rather a consequence of evolution. We suggest that this view, which we call evolved open-endedness (EOE), be incorporated more into research on open-ended evolution. This view should allow for systematic investigation of more nuanced, more concrete research questions about open-endedness and its relationship with adaptation and sustainability.

An ecological momentary assessment evaluation of emotion regulation abnormalities in schizophrenia.

BACKGROUND: Prior studies using self-report questionnaires and laboratory-based methods suggest that schizophrenia is characterized by abnormalities in emotion regulation (i.e. using strategies to increase or decrease the frequency, duration, or intensity of negative emotion). However, it is unclear whether these abnormalities reflect poor emotion regulation effort or adequate effort, but limited effectiveness. It is also unclear whether dysfunction results primarily from one of the three stages of the emotion regulation process: identification, selection, or implementation. METHOD: The current study used ecological momentary assessment (EMA) to address these questions in the context of everyday activities. Participants included 28 outpatients diagnosed with schizophrenia (SZ) and 28 demographically matched healthy controls (CN) who completed 6 days of EMA reports of in-the-moment emotional experience, emotion regulation strategy use, and context. RESULTS: Results indicated that SZ demonstrated adequate emotion regulation effort, but poor effectiveness. Abnormalities were observed at each of the three stages of the emotion regulation process. At the identification stage, SZ initiated emotion regulation efforts at a lower threshold of negative emotion intensity. At the selection stage, SZ selected more strategies than CN and strategies attempted were less contextually appropriate. At the implementation stage, moderate to high levels of effort were ineffective at decreasing negative emotion. CONCLUSIONS: Findings suggest that although SZ attempt to control their emotions using various strategies, often applying more effort than CN, these efforts are unsuccessful; emotion regulation abnormalities may result from difficulties at the identification, selection, and implementation stages.

Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood.

We investigated the development of spontaneous (resting state) cerebral electric fields and their network organization from early to late childhood in a large community sample of children. Critically, we examined electrocortical maturation across one-year windows rather than creating aggregate averages that can miss subtle maturational trends. We implemented several novel methodological approaches including a more fine grained examination of spectral features across multiple electrodes, the use of phase-lagged functional connectivity to control for the confounding effects of volume conduction and applying topological network analyses to weighted cortical adjacency matrices. Overall, there were major decreases in absolute EEG spectral density (particularly in the slow wave range) across cortical lobes as a function of age. Moreover, the peak of the alpha frequency increased with chronological age and there was a redistribution of relative spectral density toward the higher frequency ranges, consistent with much of the previous literature. There were age differences in long range functional brain connectivity, particularly in the alpha frequency band, culminating in the most dense and spatially variable networks in the oldest children. We discovered age-related reductions in characteristic path lengths, modularity and homogeneity of alpha-band cortical networks from early to late childhood. In summary, there is evidence of large scale reorganization in endogenous brain electric fields from early to late childhood, suggesting reduced signal amplitudes in the presence of more functionally integrated and band limited coordination of neuronal activity across the cerebral cortex.

Collective group drift in a partial-differential-equation-based opinion dynamics model with biased perception kernels.

In the age of technology, individuals accelerate their biased gathering of information, which in turn leads to a population becoming extreme and more polarized. Here we study a partial-differential-equation model for opinion dynamics that exhibits collective behavior subject to nonlocal interactions. We developed an interaction kernel function to represent biased information gathering. Through a linear stability analysis, we show that biased populations can still form opinionated groups. However, a population that is too heavily biased can no longer come to a consensus, that is, the initial homogeneous mixed state becomes stable. Numerical simulations with biased information gathering show the ability for groups to collectively drift towards one end of the opinion space. This means that a small bias in each individual will collectively lead to groups of individuals becoming extreme together. The characteristic time scale for a group's existence is captured from numerical experiments using the temporal correlation function. Supplementing this, we included a measure of how different each population is after regular time intervals using a form of the Manhattan and Euclidean distance metrics. We conclude by exploring how wall boundary conditions induce pattern formation initially on the most extreme sides of the domain.

Markov chain analysis indicates that positive and negative emotions have abnormal temporal interactions during daily life in schizophrenia.

Abnormalities in positive and negative emotional experience have been identified in laboratory-based studies in schizophrenia (SZ) and associated with poorer clinical outcomes. However, emotions are not static in daily life-they are dynamic processes that unfold across time and are characterized by temporal interactions. Whether these temporal interactions are abnormal in SZ and associated with clinical outcomes is unclear (i.e., whether the experience of positive/negative emotions at time t increases or decreases the intensity of positive/negative emotions at time t+1). In the current study, participants with SZ (n = 48) and healthy controls (CN; n = 52) completed 6 days of ecological momentary assessment (EMA) surveys that sampled state emotional experience and symptoms. The EMA emotional experience data was submitted to Markov chain analysis to evaluate transitions among combined positive and negative affective states from time t to t+1. Results indicated that: (1) In SZ, the emotion system is more likely to stay in moderate or high negative affect states, regardless of positive affect level; (2) SZ transition to co-activated emotional states more than CN, and once emotional co-activation occurs, the range of emotional states SZ transition to is more variable than CN; (3) Maladaptive transitions among emotional states were significantly correlated with greater positive symptoms and poorer functional outcome in SZ. Collectively, these findings clarify how emotional co-activation occurs in SZ and its effects on the emotion system across time, as well as how negative emotions dampen the ability to sustain positive emotions across time. Treatment implications are discussed.

Generation and influence of eccentric ideas on social networks.

Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label "eccentric" ideas. The eccentricity of any idea is defined as the deviation of that idea from the norm of the social neighborhood. We collected and analyzed data from two sources of different nature: public social media and online experiments in a controlled environment. We compared the popularity of ideas against their eccentricity to understand individuals' fascination towards eccentricity. We found that more eccentric ideas have a higher probability of getting a greater number of "likes". Additionally, we demonstrate that the social neighborhood of an individual conceals eccentricity changes in one's own opinions and facilitates generation of eccentric ideas at a collective level.

A two-phase model of collective memory decay with a dynamical switching point.

Public memories of significant events shared within societies and groups have been conceptualized and studied as collective memory since the 1920s. Thanks to the recent advancement in digitization of public-domain knowledge and online user behaviors, collective memory has now become a subject of rigorous quantitative investigation using large-scale empirical data. Earlier studies, however, typically considered only one dynamical process applied to data obtained in just one specific event category. Here we propose a two-phase mathematical model of collective memory decay that combines exponential and power-law phases, which represent fast (linear) and slow (nonlinear) decay dynamics, respectively. We applied the proposed model to the Wikipedia page view data for articles on significant events in five categories: earthquakes, deaths of notable persons, aviation accidents, mass murder incidents, and terrorist attacks. Results showed that the proposed two-phase model compared favorably with other existing models of collective memory decay in most of the event categories. The estimated model parameters were found to be similar across all the event categories. The proposed model also allowed for detection of a dynamical switching point when the dominant decay dynamics exhibit a phase shift from exponential to power-law. Such decay phase shifts typically occurred about 10 to 11 days after the peak in all of the five event categories.

Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials.

BACKGROUND: Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose. AIMS: Machine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables. METHODS: Participants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary assessment surveys) and passive (geolocation, accelerometry) digital phenotyping data along with clinical ratings of negative symptoms. RESULTS: Machine learning algorithms classifying the presence of a PD diagnosis yielded 80% accuracy for cross-validation in H2O AutoML and 79% test accuracy in the Recursive Feature Elimination with Cross Validation feature selection model. Models classifying the presence vs absence of clinically significant elevations on each of the 5 negative symptom domains ranged in test accuracy from 73% to 91%. A few active and passive features were highly predictive of all 5 negative symptom domains; however, there were also unique predictors for each domain. CONCLUSIONS: These findings suggest that negative symptoms can be modeled from digital phenotyping data recorded in situ. Implications for selecting the most appropriate digital phenotyping variables for use as outcome measures in clinical trials targeting negative symptoms are discussed.