Psychotherapy is a relational process that emerges from the meeting of two people. There is an ontological difference between the individual psychopathology of the patient and relational therapy; the present work aims to overcome the patient-centric conception of psychotherapy, restoring the dyadic nature of the therapy through the interpretation of the psychological interview as a fractal process. Recursion, namely the application of the same logical operator to the result of the operation itself, is presented here as the basic procedural element of psychotherapy. The paper is divided into two parts: The first has epistemological nature and focuses on complexity theory and cybernetics: Edgar Morin and recursion as a process of existence, Heinz von Foerster and epistemology as second-order praxis. From the thought of Gregory Bateson, it is here postulated the self-similarity of the content and structure of the mind, to the point of conceptualizing the dyadic relationship as a Mind of a different logical type compared to the individual mind. The second part of the present work introduces two intellectual tools designed to conceptualize psychotherapy as a fractal process: the psychopathological hologram, useful for clinical work although of a non-clinical nature, that consists in a fraction of the patient's experiential flow, while the psychotherapeutic string is presented here as the basic recursive element of psychotherapeutic process.

This study investigates the climbing dynamics of learning on a long-time scale, by using Drifting Markov models. Climbing constitutes a complex decision-making task that requires effective visual-motor coordination and exploration of the environment. Drifting Markov models, is a class of constrained heterogeneous Markov processes that allow the modeling of data that exhibit heterogeneity. By applying the later models on real-world visual motor skill data, we aim to uncover the persistent dynamics of learning in climbing. To that end a real case study is conducted based on an experiment, with results that (a) help in the understanding of skill acquisition in physically demanding environments; and (b) provide insights into the role of exploration and visual-motor coordination in learning.

In this study, we assessed the efficacy of various linear and chaotic physiological synchrony methods during collaborative emotive recall of stories, examining how physiological synchronization impacts dyadic interaction in tasks involving emotionally charged narratives. Eighty-two young individuals, forming 41dyads, participated in a task requiring the recall of stories with varying emotional content. We analyzed physiological data using the Lyapunov coefficient, cross-correlation, and coherence indices. Our statistical approach included concise applications of the student's t-test, Pearson's correlation, and notably, the receiver operating characteristic (ROC) curve. The results highlighted significant differences in physiological synchrony between emotional and less emotional situations, revealing increased synchronization in collaborative remembering of emotional stories. The integration of the Lyapunov coefficient with other indices was crucial for identifying emotional conditions, underscoring its significance in exploring emotional engagement in group memory activities. This study provides valuable insights into the dynamics of physiological synchrony in emotional interactions, its implications in cognitive and social domains, and suggests potential applications in understanding collective behavior and emotional processing.

The present study investigates the effects of performance goals, performance-approach and performance-avoidance, within the nonlinear dynamical systems perspective. The issue is revisited, by applying cusp catastrophe models on students' performance in language learning using achievement goal orientations as control variables. Data were taken from two separate studies: the first examined Ancient Greek and the second Modern Greek language, engaging 181 and 543 students respectively, both at seventh grade. The force field dynamics was the conceptual model, which was tested via cusp analysis employing the difference between the two performance goals as the asymmetry factor and their sum as the bifurcation factor, respectively. The cups models were proved superior to their linear alternatives. The findings, being in line with previous reports, establish the complex dynamical system perspective in educational psychology, whereas discussion is provided regarding the implications for current goal theories.

A debate has taken place on the relationship between challenge and skills as the universal precondition of flow. Flow's precursor, Csikszentmihalyi, states that these two constructs are independent, while other scholars state the opposite. This research aims to better understand this relationship and explore its effect on the flow experience. As flow is considered a nonergodic and nonlinear process, we will base our analysis on an intra-individual level and then shift to an inter-individual level. The database consisted of 3,630 registers collected from a sample of 60 employees. At an intra-individual level, we observed the nature of the challenge-skills relationship classifying the participants according to the direction of these relationships (positive, negative, or nonsignificant correlation). At the inter-individual level, we explored the effect that the three groups had on the flow experience. We also examined nonlinear relationships (cusp modeling) among challenge, skills, and flow. The results showed that the challenge-skills relationship is not homogeneous between individuals. Flow theory is represented by the positive correlation group, but this pattern is the least frequent (21.6% of the cases) in our sample. Finally, the results showed that the nonlinear models fit the data better (R2nonlinear = .48, R2linear = .35, p < .01).

Previous research has demonstrated that interpersonal dynamics are fractal, and that conflict is a key control parameter that drives fractal complexity. The present study aimed to extend this line of research to examine the putative fractal structure of conflict dynamics over time, and the role that this self-organizing fractal structure may play in the resilience of romantic relationships. An experience sampling methodology was used to assess levels of conflict, satisfaction, and commitment in the dating relationships of undergraduate students, three times per day for 30 days. Hypothesis 1 was supported, with conflict ratings over time generally conforming to an inverse power-law distribution (IPL) distribution. Hypothesis 2 was supported as well, with better IPL fits measured as variance accounted for (R2), predicting higher levels of satisfaction and commitment over the 30 days. Hypothesis 3 showed mixed support, with moderate network linkages (i.e., soft assembly) between conflict and satisfaction and commitment predicting higher IPL fits (the linkage of satisfaction and commitment did not predict IPL fit as predicted). Hypothesis 4 predicted that IPL fit would interact with mean conflict, buffering the impacts of conflict on mean satisfaction and commitment across the 30 days. This hypothesis was not supported; however, several statistical factors may have obscured the buffering effects of higher IPL fit and so results may be inconclusive. These methodological factors, and others, are discussed along with the potential theoretical and practical implications of the current results.

The necessity for resilient responses in occupational contexts often takes the form of unusual levels of workload that could have a dramatic impact on the performance of individuals or teams. Empirical research with the cusp catastrophe model for cognitive workload and performance, which are reviewed here, has isolated a class of variables known as elasticity versus rigidity that act as bifurcation variables in the process. Elasticity-rigidity variables derive from five sources â affect, cognitive coping strategies, conscientiousness and impulsivity, fluid intelligence, and the degrees of flexibility that are afforded by the task itself. The resilience process for work teams presents additional workload demands requiring team coordination and communication efforts and back-up, redundancy, behaviors. Finer-grained nonlinear time series analyses of performance and its surrounding events revealed that team self-efficacy varies chaotically as the team responds to a series of challenging events. The two types of dynamics combine to produce chaotic hysteresis in team performance.

Autonomic synchrony plays an important role in work team performance where coordinated actions are required on the part of the team members. The present study examined the connection between nine psychological variables that represent types of elasticity-rigidity, which are closely related to adaptability and autonomic synchrony, within teams playing a computer game that involved dynamic decision making. Elasticity-rigidity variables were first identified as part of the dynamics that transpire between workload and performance. They are used here to determine why some individuals within teams synchronize with teammates more strongly than others. The driver-empath model of group synchrony produces a single metric of synchrony (SE) within a team of three or more members. Driver scores, which are produced from the algorithm, indicate each person's total influence on the other group members. Empath scores, which are also produced from the SE algorithm, indicate a person's total receptivity to all other group members. It was found that coping flexibility, monitoring, emotional intelligence, and solving anagrams significantly predicted empath scores in the earlier part of the session. Anxiety and monitoring significantly predicted empath scores in the later part of the session. There were no significant correlations between driver scores and elasticity-rigidity variables.

The literature on organizational resilience explores various viewpoints, ranging from strategies to recover after disruptions to proactive anticipation of threats. Formal models primarily focus on the ability to recover from shocks, analyzing factors like deviation from performance targets, recovery time, and potential adaptation in function and structure. However, incorporating anticipation into such models remains scarce. Additionally, existing anticipatory systems models often neglect key aspects of organizational behavior. This work addresses these gaps by introducing an agent-based modeling approach that integrates anticipation into organizational decision-making. Our computational model features agents embedded in different organizational structures who make decisions based on projected market states (levels and trends). These decisions are subject to delays in perceiving market conditions and vary depending on the organization's adaptive capacity to update its offering. We analyze different organizational structures and market behaviors (trend direction and volatility). Our results indicate that full connectivity among agents can be detrimental to organizational resilience, as it may reduce the diversity of anticipation strategies for forecasting the market. Conversely, either sparse or highly clustered networks demonstrate a greater ability, on average, to keep up with changing market levels and trends.

This introduction to a special issue of Nonlinear Dynamics, Psychology and Life Sciences on the topic of resilience discusses the contributing articles in terms of their flexibility in methods, models, scale, and contexts combined with their integrity in shared theoretical understanding and generative knowledge. The ubiquity of resilience is discussed, a feature of potentially any living or non-living system and substance. This breadth calls for a flexible set of models and methods, along with the quest for integrative theory to make resilience science more resilient. Since resilience involves the ability of a substance or system to persist, to repair or recover, and to evolve, any common theory would consider structural integrity (the ability to hold together), flexibility (the ability to adjust and return), time and timing. Nonlinear dynamical systems theory is proposed as the only scientific perspective capable of building this sort of common knowledge of a ubiquitous process involving these specific features. The synopsis of each article's contribution to the issue includes an analysis of the flexibility the article adds in terms of models, methods, scale, and applied context, along with the theoretical integrity produced with respect to these common features of resilient processes: flexibility, integrity, time, and timing.

Resilience has traditionally been conceptualized as resisting, bouncing back from, and growing from a stressor. However, recent literature has pointed out that these are different processes with bouncing back coming closest to the literal meaning of the term resilience. To detect whether an individual demonstrates one of these three stressor-responses, different analysis strategies have been suggested. However, deeper theoretical explanations for how patterns of resistance, resilience, and growth come about, have been lacking. To address this gap, this paper proposes a coherent framework based on a dynamical systems approach. We first discuss how adapting to stressors emerges from complex interactions between multiple levels of organization within the system. These interactions unfold on different time scales: What appears as resistance on slower or macro scales may actually consist of bouncing back at micro scales that change much faster. Next, we discuss how the different trajectories that distinguish resistance, resilience, and growth can be understood through attractor dynamics. We address the fixed-point attractors, which are commonly used in the resilience literature to detect early warning signals of bifurcations following resilience losses. Moreover, we describe the implications of limit cycles and strange attractors which capture multiple pathways to adapt to stressors that can lead to growth patterns. We conclude that resisting, bouncing back from, or growing from a stressor represent distinct phenomena that can be distinguished both empirically and theoretically from a dynamical systems perspective. These distinctions may drive future development of theoretical models, empirical measurements, and theory-driven interventions.

J. Barkley Rosser Jr. dedicated his career to the study of complexity and nonlinear dynamics applied to economic matters. From his extensive work, four ideas stand out: (a) relevant economic processes can be expressed under the form of relatively simple modelling apparatus; (b) low-dimensional nonlinear systems often involve complex patterns of evolution; (c) the identified nonlinearities have significant economic meaning, and (d) there is an inherent aesthetic beauty underlying the corresponding mathematical results. This article is composed by two blocks. In the first part, the work by J. Barkley Rosser Jr. is surveyed, with a special emphasis placed on the four above-mentioned items. In the second part, a two-equation discrete-time model of diffusion of ideas is proposed and subject to analytical and graphical treatment, with the objective of illustrating how a simple dynamic model may generate complex and visually attractive dynamics with economic meaning.

This study considers dynamics generated by a three-species Lotka-Volterra competitive model with two discrete delays. The associated characteristic equation is a cubic exponential polynomial. Assuming the stability of the three-species positive stationary point in the no-delay model, we construct a stability switching curve on which the characteristic equation has a purely imaginary root. Thus, the stability may be lost. It is numerically confirmed that the stationary point bifurcates to a limit cycle via a supercritical Hopf bifurcation when the delay crosses the stability switching curve. It is also demonstrated that as the delay gets larger, two of three species are active, and the remaining one is inactive along the cycle. The birth of complicated dynamics will be discussed in our future research.

The use of predator-prey models in economics has a long history, and the model equations have largely evolved since the original Lotka-Volterra system towards more realistic descriptions of the economic dynamics of predation, competition, and synergy. Seminal examples in this regard are the business cycle model (Goodwin, 1967), chaotic hysteresis (Rosser, 1994), and the models of renewable resources (Clark, 1990). Given this background, this paper aims to analyse the mechanism of economic competition under different conditions, by adopting the unifying framework of niche models. Niche models are dynamic competition models that allow for a richer description of the interacting economic variables than the neoclassical economic theory. We conduct a qualitative study of the dynamic behaviour of firms of different types/sizes in the province di Bologna (Italy), analyzing - in discrete time - their competition through simula-tion experiments. It emerges that the economic system under analysis is coherent with different (in)stability patterns depending on different configurations of the firms within the market, by confirming the theory of industrial districts in the northeast of Italy.

The pioneering work of John Barkley Rosser Jr. (1948-2023) in various subfields of economics emphasizes the fact that economic and social phenomena are inherently nonlinear and often discontinuous. From this standpoint, Barkley has contributed substantially to a paradigm shift in economic theory and modelling. Both his influential research work and his unceasing survey work on different approaches and schools of thought in economics and social science, carried out through the lens of complexity theory, have succeeded to develop a broader view on economic thinking and continue to inspire many researchers worldwide. The articles in this issue cover a number of research areas and themes that were central to Barkley's work, from technological progress to evolutionary competition between firms, from regional science to income inequality, from environmental economics to more general macroeconomic themes, such as bubbles and crashes, financial instabilities and policy issues.

In this paper, we study the complex interaction between environmental damage and location in space of firms and entrepreneurial households. We use a New Economic Geography (NEG) framework, suitably extended to account for environmental damage and the two mobility processes. The resulting model is a two-dimensional piecewise smooth map with two constraints for each variable, and we use analytic and numerical tools to explore its long-run dynamics. We pay special attention to the different types of fixed points and the structure of the respective basins of attraction. Their complexity re-enforces a core theme of the NEG: History matters for the long run location in space of economic activity.

We develop a foreign exchange market model in which a market maker adjusts the exchange rate with respect to the trading behavior of chartists, fundamentalists and a central bank. While chartists bet on the persistence of bull and bear markets, fundamentalists speculate on mean reversion. The central bank seeks to stabilize the foreign exchange market by placing buy (sell) orders when the undervaluation (overvaluation) of the exchange rate exceeds a certain threshold. Since a one-dimensional piecewise-linear discontinuous map with three branches determines the evolution of the exchange rate, we use a combination of analytical and numerical tools to explore the extent to which the central bank is able to tame the behavior of the foreign exchange market.

In this work, our objective is to explain the decline in the income share of the middle class and the increase in the share of the wealthy - a global empirical phenomenon, commonly referred to as 'The Elephant' (Lakner & Milanovic, 2013; Milanovic, 2016) - by examining the different life-cycle income paths of heterogeneous income classes and the varying tax burden on labor and capital income. The model investigates the diverse life-cycle paths and their nonlinear behavior among the income classes under scrutiny. This approach enables us to dynamically analyze the divergence in the income distribution using one of the more important models in economics: The life-cycle and permanent-income framework.

The U.S. Federal Reserve now controls a part of the money supply, but other financial institutions, called the 'shadow' banks, issue a growing amount of the money supply, which remains outside the control of the U.S. Federal Reserve. Being unregulated, these shadow banks operate by offering highly risky amounts of credit, leading to lack of confidence and consequent run on such banks. However, due to competition with the shadow banks, the commercial banks have undergone structural change and are themselves engaged in trading the same financial assets as traded by the shadow banks. Hence the distinction between banks and shadow banks is now moot. Consequently, almost all large financial institutions operate like the shadow banks, and now are heavily engaged in speculative derivative futures trades. The second structural change is that the derivatives market now dominates the prices not only of financial futures but also the prices of all traded commodities, soft and hard, demonstrating oligopolistic market power. The unchecked growth of speculative activity in the futures markets has raised commodity prices and also increased price volatility. This in turn has rendered the entire financial system including the banking system to become unstable, leading to bank runs and financial 'bubbles.'

Sustainable pumping of water resource requires intervention by a public agency in order to avoid over-exploitation. We study the evolution of compliance and regulation of groundwater resource when farmers can decide whether to comply or not with pumping quotas in an imitation rule described by replicator dynamics. The public agency sets the optimal quotas and the farmers can choose between compliance or violation of them. We investigate the policy of the public agency which may impose sanctions to discourage withdrawals that deviate from the optimal quota. Using numerical simulations, we analyze the effects that parameters have on the equilibrium of the aquifer and on the farmers' behavior.

The spread of epidemics over a landscape of several population agglomerations is presented. A continuous, system dynamics version of an epidemics model is discussed and compared to the agent-based model. The validity of the continuous Susceptible-Infected-Recovered-Susceptible (SIRS) model is questioned. The main model deficiencies are the lack of the influence of the collective memory of the population and the spatial distribution of individuals. The chaotic behavior of the agent-based model is pointed out as a better approximation of the true dynamics of an epidemic.

Posttraumatic Growth (PTG), characterized by newfound meaning, perspective, and purpose for trauma survivors, remains enigmatic in its nature. This state is thought to arise from the dynamic interplay of biopsychosocial factors; however, the nature of this interplay is unclear. This study aimed to investigate the intricate relationship between PTG and facial affect dynamics, shedding light on the complex interplay of biopsychosocial factors that underpin this transformative process. We conducted a comprehensive investigation involving 19 wildfire survivors who provided daily self-reported PTG ratings alongside smartphone videos analyzed using Automated Facial Affect Recognition (AFAR) software. Our findings revealed compelling evidence of self-organization within facial affect, as indicated by notably high mean R2 and shape parameter values (i.e., nonlinear indices indicative of structural integrity and flexibility). Further regression analyses unveiled a significant interaction between the degree of facial affect 'burstiness' and coping self-efficacy (CSE) on PTG. This interaction suggested that PTG development was a nuanced process intricately linked to the coherence of emotion patterns exhibited by individuals. These insights illuminate the multifaceted dynamics at play in the emergence of PTG and contribute to a broader understanding of its biopsychosocial foundations.

In this paper, we focus on the nonlinear dynamic behavior of fractional order love model because the fractional order can reflect the 'memory dependency' of certain dynamic processes to a certain extent. The novel fractional order love model without external environment effect investigates two aspects: first, the chaotic dynamic of the used system when the system order is 2, and second, the smallest system order of fractional order love model that can generate chaotic behaviors. The simulation results show the fractional order love model can produce different results compared to the integer order model. While the fractional order love model still has chaotic behavior even the sum of the system order is equal to 2. Moreover, the smallest system order of fractional order love model having chaotic behavior is 1.7. The results indicate that two individuals can display love status even if the sum of the system order is less than 2 because the 'memory dependency' effects can greatly affect the emotional changes of human beings. The simulation results based on time series, phase portrait, power spectrum, Poincare map, maximal Lyapunov exponent and bifurcation diagram, and the conclusion is applied to the real life are also discussed.

This study evaluated cusp models of workload and fatigue experienced by teams on a dynamic decision making task. Cognitive workload is the amount of information that a person is required to process in a given way in a fixed amount of time. Fatigue, which is captured by a work curve or a cubic polynomial function, is the loss of work capacity that is produced by an extended amount of time spent on a particular cognitive or physical task. In this experiment, 32 groups of three, four or five members (136 individuals) played two matches of a first-person shooter computer game, and completed subjective measures of workload and cognitive measures of elasticity versus rigidity. For the workload cusp models with elasticity-rigidity components, the bifurcation in performance levels occurred when teams expressed greater emotional intelligence, anxiety, levels of fluid intelligence, coping flexibility, cognitive flexibility, and were more decisive (R2=.54-.56, linear alternative, .09-.23). For workload cusp models assessing subjective ratings of workload, bifurcation occurred with groups who reported greater levels of performance demand and effort required (R2=.51, linear alternative, .20). For fatigue cusp models, bifurcation occurred for groups that played fewer rounds of the game before winning or losing the match, or came from the smaller-sized groups, which were supplemented by computer-generated agents (R2=.66-.67, linear alternative, .21-.68). Results supported the general-ization of the cusp models for workload and fatigue to situations requiring teamwork in dynamic decision making environments. The study also raised new questions about the role of autonomic synchrony in the workload or fatigue processes and similarity of the dynamics of human-autonomy teams compared to all-human teams.

This year's cover artists are members of a team of physicists and psy-chologists who create human-centered designs based on psychology experiments that investigate the positive impacts of viewing fractal patterns. These positive impacts include reduced physiological stress levels and enhanced cognitive skills. Here, the team explores the concept of 'fractal iconography' as an approach to employing computers to generate naturalistic art. Adopting this approach, three forms of fractal patterning ('fractal icons') are combined in a variety of ways to generate the rich complexity of nature's scenery. These composite fractals are remarkably effective at conveying nature's aesthetic power.

In a recent article, we presented evidence demonstrating the existence of hidden y-stories within the genomes of humans and canines. These stories were found not only in the non-protein-coding regions but also within the genetic regions and the sequence of exons. Consequently, we are now exploring whether these discoveries are unique to humans and dogs or if they are more widely distributed throughout the cellular world. To approach this question, we embarked on an investigation of the genomes of various species across Whittaker's five kingdoms, namely Animalia, Plantae, Fungi, Protista, and Monera. Through online resources, we obtained and analysed whole-genome sequences of one avian species, one fish species, one reptile species, and one invertebrate species within the Animalia kingdom. Furthermore, we examined the genomes of one plant species, one fungus species, one protozoan species, and two bacterial species. Employing the same methods as in our prior studies, our findings in this study align with our proto knowledge hypothesis, suggesting that all living cells possess a repository of hidden y-information which determines the cellular design, sustains its overall functionality, and governs its performance and behaviour throughout its lifespan until death. We briefly explain life as a bio-linguistic phenomenon and future projects.

The Hermite wavelet method (HWM) is introduced in this study to solve a nonlinear differential equation determining the human corneal morphology. The changes in curvature of the human cornea in hypotony, normal intraocular pressure, glaucoma, and other conditions are discussed. The Hermite wavelet operational matrices of derivatives are used to generate wavelet solutions based on this technique. The solutions of the nonlinear differential equation are determined for various values of constant parameters that can appear in the diverse physical situations. The proposed wavelet solutions are more accurate than the other approximate analytical solutions listed in the literature. The HWM solutions are compared to homotopy perturbation method, Taylor series, pertur-bation technique and artificial neural network solutions. There is broad consensus. This illustrates that HWM is a useful and appropriate strategy for handling difficulties with nonlinear boundary value problems that emerge in corneal geometry.

Human resilience is often considered as static traits using a reductionist approach. More recent work has demonstrated it to be a dynamic and emergent property of complex systems. This narrative review explores human resilience through a self-organizing framework with a specific emphasis on the application of nonlinear modeling approaches. Four classes of approaches are examined: univariate dynamics, bivariate coupling, topological modeling, and network modeling. Univariate dynamics capture the temporal structure and flexibility within a single time series, while bivariate coupling approaches quantify the interaction dynamics and coordination between two time series. Topological modeling identifies bifurcations and attractor dynamics as signals of critical transitions relative to emergence and system stability. Network modeling represents system structure with a focus on connectivity, flexibility, and system integrity. Applying a complex systems framework, this review provides insights into data modeling opportunities for characterizing important features of a system's capacity to bounce back and recover from stress. These characteristics are connected to meta-flexibility, which characterizes a system's adaptive responsiveness to stressors, including post-traumatic growth, and the relation between meta-flexibility and metastability is discussed. Overall, this review provides a foundation of tools for researchers interested in under-standing human resilience through a complex systems framework.

Situation awareness (SA) is a mental state that is instrumental to performance of complex dynamic tasks. SA within teams is thought to be supported by favorable social conditions within the team. The present study was organized in two parts: (a) causal relationships among SA, group cohesion, and autonomic synchrony, the latter being a fundamentally nonlinear process, and (b) the combined impact of the three variables on performance in a dynamic decisions task. Experimental conditions assessed changes in task difficulty, group size, and method of obtaining SA measures. Participants were 136 undergraduates organized into 32 teams of three to five members engaged in two matches of a first-person shooter computer game. They completed self-report measures of cohesion and SA. Synchrony was determined through time series analysis of electrodermal responses using the driver-empath framework. ANOVA results showed that cohesion and SA improved over the two matches, and SA was better in smaller groups during the second match. Synchrony was stronger in larger groups. Granger regression indicated no causal or circular relationship between SA and cohesion. Synchrony had a small positive effect on cohesion during the first match. SA had a strong negative impact on synchrony early on and dissipated afterwards. The best performing teams during the first match were those that: were larger, were measured for SA without pausing the simulation, were less synchronized, showed more accurate SA, and reported stronger cohesion. The study opens new questions concerning the role of synchrony in volatile situations and the role of automated team members operating alongside humans.

Theories and studies of corruption typically focus on individual ethics and agency problems in organizations. In this paper, we use concepts from complexity science to propose a process theory that describes how corruption risk emerges from conditions of uncertainty that are intrinsic in social systems and social interactions. We posit that our theory is valid across multiple levels of scale in social systems. We theorize that corruption involves dynamics that emerge when agents in a system take actions that exploit disequilibrium conditions of uncertainty and ethical ambiguity. Further, systemic corruption emerges when agent interactions are amplified locally in ways that create a hidden value sink which we define as a structure that extracts, or 'drains,' resources from the system for the exclusive use of certain agents. For those participating in corruption, the presence of a value sink reduces local uncertainties about access to resources. This dynamic can attract others to join the value sink, allowing it to persist and grow as a dynamical system attractor, eventually challenging broader norms. We close by identifying four distinct types of corruption risk and suggest policy interventions to manage them. Finally, we discuss ways in which our theoretical approach could motivate future research.