Topology as Biological Code: Information-Driven Evolution of Neighborhood Structures in Self-Organizing Cellular Systems.

In the Spring 2025 issue of the Journal of Scientific Exploration (JSE), Ciaran O’Keeffe, Brandon Massullo, Brian Laythe, Neil Dagnall, Kenneth Drinkwater, and James Houran (2025; hereafter O’Keeffe et al.) published an article in which they claim validity for their Haunted People Syndrome (HP-S) model of “ghostly episodes” in the features of the Silicon Valley case investigated and reported by the present authors (Auerbach et al., 2022a, 2022b, 2023), involving household disturbances of the recurrent spontaneous psychokinesis (RSPK, or “poltergeist”)-type (video presentation available here). We take issue with a number of O’Keeffe et al.’s claims on logical, empirical, conceptual, and clinical grounds, and since our counterarguments are too extensive to meet the 10,000 word limit allotted to us by the JSE, we instead present them in the collection of papers contained herein, prefaced by introductory remarks from L.A. The paper by B.J.W. points out several empirical and conceptual issues with HP-S before specifically addressing the arguments made by O’Keeffe et al. (2025) in their article text. In particular, O’Keeffe et al. seem to misunderstand the anomalous episodic communal kinetic occurrences (AECKO) methodological approach to spontaneous cases being developed by G.F.S., labeling it a “model” when it is not meant to be. O’Keeffe et al. also continue to use the term “poltergeist,” claiming it as a member of their (undefined) “ghostly episodes” phenomena, while ignoring historic evidence suggesting that “poltergeist”/AECKO/RSPK-type cases are a distinct class of their own. O’Keeffe et al. further seem to overlook parapsychological study findings which indicate that several of the behavioral traits, states, and characteristics they associate with HP-S also tend to be correlated with veridical psi, and would thus not uniquely support HP-S alone. And rather than being consistent with HP-S, the qualitative features of the Silicon Valley case actually appear to be more at odds with HP-S when examined closely, thereby challenging O’Keeffe et al.’s claim of validity. B.H.’s paper challenges O’Keeffe et al.’s (2025) use of clinical data from Auerbach et al.’s (2022, 2023) Silicon Valley case study to validate their Haunted People Syndrome (HP-S) theory. Potential ethical issues are considered due to what appears to be a possible misinterpretation or seeming misrepresentation of Auerbach et al.’s data, arguing that O’Keeffe et al. (2025): 1.) enlisted a member of their team (a ‘professional clinician’) to supply subjective rating responses to their HP-S questionnaires; 2.) used Auerbach et al.’s Highly Sensitive Person (HSP) Self-Assessment psychoeducational clinical intervention data out of context for clinical assessment of the family; and 3.) made clinical assessment/interpretations about family members, based on the HSP data and their own team generated HP-S data, without the family’s informed consent. B.H. notes that the research analysts and clinicians in the O’Keeffe et al. and Auerbach et al. teams are from multiple countries (U.S., U.K., and Canada), each with distinct standards of practice and codes of ethics. B.H. concludes with suggesting that parapsychologists adhere to the Universal Declaration of Ethical Principles for Psychologists as proposed to and adopted by the International Union of Psychological Science’s (IUPsyS, 2008) 116 national psychology associations world-wide to forward the development of ethical standards of practice in the emerging specialty field of clinical parapsychology. G.F.S.’s paper raises two additional considerations: First, rather than parapsychology, O’Keeffe et al.’s (2025) article seems to emerge from anomalistic psychology, where anomalous experiences are explained in terms of known physical and psychological factors, often to the exclusion of any veridical psi element possibly being involved. This would seem to be most clearly evidenced by their article continuing to maintain a long-held working assumption that psi experiences are ultimately delusional, in spite of existing data to the contrary. Second, it is argued that the reductionist methods traditionally used in parapsychology may be inadequate for understanding psi, and that the approach to studying psi might benefit from being reframed in terms of complex systems science. This would necessitate a greater respect for spontaneous case research, including AECKO investigations.

Anticipating unintended consequences of public health nutrition policy: Implications for disordered eating and weight stigma.

Quantum Molinism is a contemporary theological model that builds upon classical Molinism to address the enduring tension between divine sovereignty and human freedom. Situated within the tradition of middle knowledge theories, it advances a probabilistic reinterpretation of divine omniscience informed by quantum uncertainty, chaos theory, and complex systems science. This model proposes that God, while fully sovereign and omniscient, has created a dynamic universe in which human choices are genuinely free yet bounded within a structured probability matrix. Crucially, this openness is not merely epistemic but ontological: a divinely embedded ‘quantum sliver’ of contingency woven into the fabric of creation itself. Such a framework preserves real contingency, safeguards the meaningfulness of prayer, secures the authenticity of love, and maintains the moral responsibility of creatures. By reframing Gottfried Wilhelm Leibniz’s claim that this is the ‘best of all possible worlds’, Quantum Molinism suggests that ‘best’ is not measured by the absence of pain, but by the possibility of agapē freely chosen. Contribution: The article culminates with the Agape Triangle, illustrating how truth, love, and freedom converge within the moral ecology of the universe to orient human life toward Christlikeness.

Reductionism has underpinned modern science since the 17th-century Scientific Revolution. This methodology decomposes systems into minimal units and deduces wholes from parts, succeeding across physical, life and social sciences. However, reductionism reveals limits as the scientific frontier shifts from identifying building blocks to understanding how components generate collective behavior. We possess massive data and precise local equations but fail to predict cell fates, financial crises or cognitive emergence in neural networks. Complex systems science-a mid-20th-century discipline exploring the structures, behaviors, evolution and laws of complex systems-integrates with diverse fields to transform scientific paradigms. Rapid AI development triggers this paradigm shift. AI algorithms handle massive data and complex systems, while AI systems themselves constitute complex systems whose progress depends on complex systems science. In this NSR Forum, we convene five researchers to discuss the essence of complex systems science and its integration with other disciplines to support a pivotal scientific paradigm shift in the AI era. Tingting Gao Postdoc Researcher, Network Science Institute, Northeastern University, USA Wei Lin Professor, School of Mathematical Sciences and Research Institute of Intelligent Complex Systems, Fudan University, China Yu Liu Associate Professor, Department of Systems Science, Beijing Normal University at Zhuhai, China Jiang Zhang Professor, School of Systems Science, Beijing Normal University, China Lei Guo (Chair) Professor, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China.

Tuberculosis persists as the world's deadliest infectious disease, despite improved diagnostics and effective treatment. The tuberculogenic environment describes the sum of influences, vulnerabilities, policies, life conditions, and health factors that sustain the tuberculosis pandemic in vulnerable communities. The persistence of these environments is attributable to challenges upstream of the health system, involving sectors such as trade, taxation, finance, agriculture, employment, social services, and education. The availability, affordability, access, and acceptability of safe infrastructure (including housing), nutritious foods, protection against harmful consumption (tobacco, alcohol, sugar, etc), and adequately resourced health services are all linked to tuberculosis risk. Yet people affected by tuberculosis and national tuberculosis control programmes continue to bear almost the sole responsibility for a problem that is largely beyond their control. Reframing tuberculosis through the lens of complex systems science highlights the array of decision makers who, by action or inaction, have a shared responsibility to end tuberculosis as a global pandemic.

Game theory and complex systems represent two pivotal and increasingly intersecting fields in contemporary scientific research. While game theory provides a structured mathematical framework for understanding decision-making under conditions of conflict and cooperation, complex systems science offers the tools to analyze the intricate, non-linear interdependencies that characterize the real world-from biological ecosystems to socioeconomic structures. By weaving game theory into the fabric of complex systems, researchers can decipher not only the adaptive strategies of individual agents but also the macroscopic evolutionary trajectories that emerge from their interactions. This Research Topic, "Game Theory and Evolutionary Dynamics: Unraveling Complex Systems," was curated to advance our comprehension of how emergent phenomena are born from strategic interactions. We sought to move beyond conventional assumptions of complete rationality, encouraging models that reflect the nuanced realities of limited information, bounded rationality, and dynamic network structures. The resulting collection of five articles offers a diverse yet cohesive exploration of these themes, spanning primate evolution, public health policy, organizational management, and competitive sports.The contributing articles can be broadly categorized into three thematic areas: evolutionary foundations of cooperation, dynamics of social and organizational adaptation, and strategic optimization in competitive environments.MacLaren et al. provide a foundational perspective by investigating the Social Brain Hypothesis through the lens of evolutionary game theory. In their article, "Cooperation and the social brain hypothesis in primate social networks," the authors combine data on primate brain size with theoretical models of cooperation on networks. They utilize a gift-giving game framework to demonstrate that species with larger brain sizes tend to form social network structures that more effectively foster cooperation. This work highlights the critical role of network reciprocity in the evolution of social complexity, offering empirical support for the idea that cognitive capacity and social structure co-evolve to solve collective action problems.Moving from biological to human social systems, two articles employ dynamical modeling to understand adaptation and policy impact.In "How do policy tool combinations drive the construction of public health technology R&D alliances?", Cao et al. construct a tripartite evolutionary game model involving government bodies, pharmaceutical enterprises, and research institutions. Their analysis of supply-side, demand-side, and environmental policy tools reveals that demand-side government procurement is the most effective incentive for fostering alliances. Crucially, they identify non-linear threshold effects, showing that excessive intervention can paradoxically reduce participation-a classic complex system phenomenon where "more is not always better."Similarly, Hailu et al. apply advanced mathematical control theory to organizational psychology in "Insight into employees' perceptions on reform initiatives in public service organizations using fractional order derivatives with optimal control strategies." By modeling employees as "indifferent," "resistant," or "adaptive," they use fractional calculus to capture the memory effects and dynamics of opinion change. Their application of optimal control theory suggests that targeted training and clear communication are the most effective strategies to minimize resistance, providing a rigorous quantitative basis for change management.The final two articles explore optimization and performance in competitive physical and virtual arenas. Gullholm et al. offer a fascinating intersection of sports analytics and game theory in "Diversity is key: fantasy football dream teams under budget constraints." By analyzing "dream teams" in Fantasy Premier League under various constraints, they find that top-performing teams exhibit a high degree of diversity across multiple variables-a feature not found in randomly assembled teams. This finding suggests a "game theoretic" advantage to diversity, paralleling biological principles where diversity enhances system robustness and evolutionary fitness. Together, these five articles illustrate the immense versatility of game theory and evolutionary dynamics as analytical lenses. Whether explaining the roots of primate cooperation, optimizing public health alliances, managing organizational resistance, or assembling competitive sports teams, the underlying mathematical principles reveal a common truth: complex systems are driven by the continuous interplay of strategic adaptation and structural constraints. We hope this collection inspires further interdisciplinary research that bridges the gap between theoretical models and real-world complexities.

First defined by Arthur Tansley approximately 90 years ago, ecosystems arguably remain less monitored and understood than other dimensions of biodiversity. While our characterisation and understanding of ecosystems has improved, many of the issues pointed out by Tansley remain unresolved. We highlight how combining geodiversity research and complex system science, while better integrating cultural diversity and research outcomes across biodiversity dimensions, could help to address these issues and rapidly improve our ability to predict long-term ecosystem behaviour and dynamics. As ecosystems continue to gain traction in policy spheres, such knowledge will be key to strengthening the legal framing for biodiversity conservation, use, and management.

Urban neighborhood inequities in health remain a persistent public health issue, despite many efforts to promote health equity. Given the complex nature of these inequities, a complex systems science approach is essential to identify and understand their underlying causes. In this study, a causal loop diagram (CLD) was developed to visualize the underlying mechanisms contributing to urban neighborhood inequities in self-assessed health. The CLD was based on the results from a scoping literature review and the input from an interdisciplinary group of researchers. Three overarching themes were identified: 1) Uneven power dynamic in decision-making, 2) Socioeconomic sorting through environmental factors, 3) Mutual reinforcement of social cohesion and the physical environment. The interplay of these themes demonstrates that addressing neighborhood inequities in health requires an integrated approach that is inclusive in the decision-making processes and empowers communities.

ABSTRACT Contemporary logistics and supply chain management (L/SCM) face unprecedented disruption from emerging technologies, rising complexity, and escalating uncertainties, in which traditional approaches to value creation have limited reach. In this vein, this paper introduces a cross‐disciplinary theoretical toolkit that draws on unconventional (or less commonly used) theories from sociology, ecology, neuroscience, and complex systems science to advance the L/SCM conversations. We introduce novel perspectives on power dynamics, resilience, flow optimization, and decision‐making under uncertainty. These perspectives advance a clearer understanding of disruptive value creation, defined here as the generation of adaptive, inclusive, resilient, and ecologically integrated forms of value enabled by disruptive technologies and cross‐disciplinary reconfiguration of logistics systems. To guide both research and practice, we outline future‐oriented questions that provide a roadmap for pioneering strategies in L/SCM. The paper calls for a shift beyond efficiency‐driven paradigms toward adaptive, inclusive, and disruptive value creation. Thus, this paper seeks to inspire academic conversations while supporting managers with frameworks to guide and transform fast‐changing supply networks.

Introduction Copper has the dual attributes of industrial raw materials and financial assets, and its price formation mechanism presents complex non-linear characteristics under the dual role of supply and demand mechanism and financialization. The structural upgrading of industrial demand and the risk contagion effect in the futures market make it difficult to effectively analyze the fluctuation characteristics of copper price in the traditional linear analytical framework. Consequently, it is significant to explore the fluctuation characteristics of copper futures price from the perspective of complex system science. Methods This study employed complex network theory and the Markov switching model to develop a Markov network model of copper futures and to explore the evolving characteristics of copper prices. Results and discussion This study finds that: (1) There are 243 price switch states in theory, but only 126 types of states actually occur. Among them, 33 high-frequency states account for 90% of the total number of times, indicating that price fluctuations are active and concentrated in a regular manner. (2) The average path length of network state transition is 5.4, and the symmetry coefficient is 0.99, which shows that the transition efficiency is high but the path is highly asymmetric. (3) There are some nodes with low degree centrality and high betweenness centrality in the network, which act as mediators in the network, connecting the transitions between states. (4) The network has a significant association structure, and we find that the state nodes have a relatively obvious “rich club” effect. This study reveals that the nonlinear dynamics and network structures of copper future return.

Improving the healthcare system is a persistent and pressing challenge. Collaborative Learning Health Systems, or Learning Health Networks (LHNs), are a novel, replicable organizational form in healthcare delivery that show substantial promise for improving health outcomes. To realize that promise requires a scientific understanding that can serve LHNs’ improvement and scaling. We translated social and organizational theories of collaboration to a computational (agent-based) model to develop a computer simulation of an LHN and demonstrate the potential of this new tool for advancing the science of LHNs. Model sensitivity analysis showed a small number of parameters with outsized effect on outcomes. Contour plots of these influential parameters allow exploration of alternative strategies for maximizing model outcomes of interest. A simulated trial of two common health system interventions – pre-visit planning and use of a registry – suggested that the efficacy of these could depend on LHN current state. By translating heuristic theories of LHNs to a specifiable, reproducible, and explicit model, this research advances the scientific study of LHNs using tools available from complex systems science.

Cooperation at scale is critical for achieving a sustainable future for humanity. However, achieving collective, cooperative behavior—in which intelligent actors in complex environments jointly improve their well-being—remains poorly understood. Complex systems science (CSS) provides a rich understanding of collective phenomena, the evolution of cooperation, and the institutions that can sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity and environmental context—largely for the sake of tractability and because it has not been clear how to do so rigorously. These elements are well captured in multiagent reinforcement learning (MARL), which has recently put focus on cooperative (artificial) intelligence. However, typical MARL simulations can be computationally expensive and challenging to interpret. In this perspective, we propose that bridging CSS and MARL affords new directions forward. Both fields can complement each other in their goals, methods, and scope. MARL offers CSS concrete ways to formalize cognitive processes in dynamic environments. CSS offers MARL improved qualitative insight into emergent collective phenomena. We see this approach as providing the necessary foundations for a proper science of collective, cooperative intelligence. We highlight work that is already heading in this direction and discuss concrete steps for future research.

Mathematical modelling of infectious disease spreading on temporal networks has recently gained popularity in complex systems science to understand the intricate interplay between social dynamics and epidemic processes. As analytic solutions can usually not be obtained, one has to resort to exact stochastic simulation algorithms, yet these have remained infeasible for the large sizes of realistic systems. Here, we introduce a rejection-based stochastic sampling algorithm with high acceptance probability (‘high-acceptance sampling’; HAS), tailored to simulate disease spreading on adaptive networks. We prove that HAS is exact and can be multiple orders faster than Gillespie’s algorithm. While its computational efficacy is dependent on model parameterization, we show that HAS is applicable regardless on whether contact dynamics are faster, on the same time-scale, or slower than the concurrent disease spreading dynamics. The algorithm is particularly suitable for processes where the spreading- and contact processes are co-dependent (adaptive networks), or when assumptions regarding time-scale separation become violated as the process unfolds. To highlight potential applications, we study the impact of diagnosis- and incidence-driven behavioural changes on virtual Mpox- and COVID-like epidemic and examine the impact of adaptive behaviour on the spreading processes.

Abstract Nonlinear equations play a fundamental role in explaining complex systems in science and technology, particularly in the field of wave propagation. Nonlocal elasticity theory is a general method for analyzing nanostructures at the nanoscale. The current work utilizes Eringen's nonlocal constitutive equations to solve the nonlinear equations of motion for nanorods. The equation of motion for a nanorod embedded in a viscoelastic medium is obtained from the Lagrangian formulation. This study examines the behavior of weakly nonlinear, dispersive, and dissipative waves using the reductive perturbation technique (RPT). It also explores localized traveling wave solutions for these field equations. The current work employs carbon nanotubes’ (CNTs) geometric and physical properties to determine the existence of solitary wave solutions and numerically investigate the nonlocal, nonlinear, dissipative, and dispersive effects on these equations.

Coaxing out desired behavior from pretrained models, while avoiding undesirable ones, has redefined Natural Language Processing (NLP) and is reshaping how we interact with computers. What was once a scientific engineering discipline—in which building blocks are stacked one on top of the other—is arguably already a complex systems science—in which emergent behaviors are sought out to support previously unimagined use cases. Despite the ever increasing number of benchmarks that measure task performance, we lack explanations of what behaviors language models exhibit that allow them to complete these tasks in the first place. We argue for a systematic effort to decompose language model behavior into categories that explain cross-task performance, to guide mechanistic explanations and help future-proof analytic research.

Abstract This study explores the existence, uniqueness, and Ulam-Hyers stability of solutions for coupled Atangana-Baleanu-Caputo fractional differential equations with time-dependent delays and integral boundary conditions. These equations, characterized by nonlocal and nonsingular kernels, effectively capture memory and hereditary properties essential for modelling complex systems in science and engineering. To establish the existence of at least one solution, Schaefer’s fixed point theorem is employed, while uniqueness is demonstrated using Banach’s fixed point theorem. A key contribution of this research is the formulation of novel Ulam-Hyers stability criteria, ensuring the small deviation in initial conditions yields bounded variations in the solution, thereby confirming the robustness of the system. An illustrated example is provided to validate the theoretical findings. This work expands the Ulam-Hyers stability framework of the complex group of fractional integro-differential equations, filling in gaps in existing research and improving the use of mathematical models that involve fractional-order dynamics with delays and integral limits.

Background Alcohol-related harms – including acute and chronic harms as well as those partially- and fully-attributable to alcohol consumption – incur a substantial public health burden. Understanding the potential associations between structural factors, such as housing eviction, and alcohol-related harms could elucidate important opportunities for reducing this burden. Therefore, the objective of this review is to assess what current research shows about the associations between eviction and alcohol-related harms. Methods Using PubMed/Medline, APA PsycINFO, and Web of Science, we conducted a critical review of original research articles published in English January 1, 2000-March 8, 2024 that examined the associations between housing eviction and fully alcohol-attributable and acute partially alcohol-attributable harms. Of the 353 unique articles identified, 28 met inclusion criteria and three additional articles were identified through a descendancy search. Results The search yielded 31 articles, with over half published since 2021. Current research documents positive associations between housing eviction and acute partially-alcohol attributable harms, specifically, suicide/suicide risk, intimate partner violence, and violent crime. Only six studies examined alcohol consumption or fully alcohol-attributable harms, such alcohol use disorder, with four of these studies finding a positive association. Conclusions Although there is some evidence supporting positive associations between eviction and alcohol-related harms, particularly those that are acute and partially alcohol-attributable, substantial gaps remain. Recommendations for future research include: elucidating the role of alcohol consumption in amplifying risk of harm; further examining the association between eviction and fully alcohol-attributable harms, especially AUD; and, employing community-partnered, strengths-based, multilevel, and complex systems science approaches.

Social evolutionary theory seeks to explain increases in the scale and complexity of human societies, from origins to present. Over the course of the twentieth century, social evolutionary theory largely fell out of favor as a way of investigating human history, just when advances in complex systems science and computer science saw the emergence of powerful new conceptions of complex systems, and in particular new methods of measuring complexity. We propose that these advances in our understanding of complex systems and computer science should be brought to bear on our investigations into human history. To that end, we present a new framework for modeling how human societies co-evolve with their biotic environments, recognizing that both a society and its environment are computers. This leads us to model the dynamics of each of those two systems using the same, new kind of computational machine, which we define here. For simplicity, we construe a society as a set of interacting occupations and technologies. Similarly, under such a model, a biotic environment is a set of interacting distinct ecological and environmental processes. This provides novel ways to characterize social complexity, which we hope will cast new light on the archaeological and historical records. Our framework also provides a natural way to formalize both the energetic (thermodynamic) costs required by a society as it runs, and the ways it can extract thermodynamic resources from the environment in order to pay for those costs — and perhaps to grow with any left-over resources.

The growing importance of football methodology has led to the creation of specialized departments within professional clubs. Spanish professional football academies, popular around the world, have been pioneers in introducing holistic and complex systems approaches to training methodologies, which initially sparked some controversies and caused epistemological confusion. This study aims to identify the methodological approaches used by Spanish professional football academies, and explore their theoretical and practical coherence. Nine methodology department coordinators, representing a cross-section of the target sample, voluntarily agreed to participate in the study. They responded a semi-structured interview covering: (a) the theoretical foundations of their training methodologies, (b) the consistency between the theoretical principles and the practical applications, and (c) the interactions among specialists from different club sections and the alignment between the methodologies applied in the academy and the first team. The interviews were analyzed using reflexive thematic analysis (RTA). A qualitative content analysis revealed a dominant lack of epistemological coherence both among and within participants, a disconnect between their theoretical frameworks and practical applications, and an inexistent or insufficient dialogue among experts and sections within the same club, along with a lack of alignment between the methodologies applied in the academy and the first team. These inconsistencies and lack of coordination may hinder both players performance and health. In conclusion, coaches’ education in complex systems science is proposed to clarify epistemological inconsistencies, empower them to create and contextualize training methodologies, bridge the gap between theory and practice, and enhance collaboration with other professionals.