Modeling Individual Risk Decision-Making: A Self-Organization Based Psychological Game Framework [F(T, P, C, R)]

Abstract

Modernizing public security risk governance demands a paradigm shift from reactive response to proactive, systems-oriented prevention. Prevailing governance models, with their focus on institutions and technology, often neglect the micro-foundational mechanisms of risk generation: the internal psychological processes of individuals. To address this gap, this study develops a novel theoretical model—the F(T, P, C, R) framework—which integrates self-organization theory with a psychological gaming perspective. We conceptualize an individual’s behavioral choice (F_behavior) as an emergent outcome of the dynamic interplay among four constitutive factors: the situational context of Time (T) and Place (P), and the cognitive assessments of perceived Risk Control power (C) and perceived Risk Destructive power (R). Employing automotive driving behavior—specifically decisions regarding safe distance maintenance and the adoption of autonomous driving technologies—as our primary analytical scenario, we derive a dynamic risk-decision matrix. This matrix categorizes behavioral outcomes into four distinct quadrants (Confirm, Tend-to-Confirm, Tend-to-Deny, Deny) based on the subjective calculus between C and R, thereby elucidating the internal logic of risk-related choices. The study’s main contribution is constituted by this novel micro-behavioral analytical framework that integrates cognitive science with systems-based governance principles. It offers theoretical insights for behavioral public policy and provides a structured toolkit for diagnosing and designing targeted interventions, ultimately aiming to enhance proactive risk management and systemic resilience.

1. Introduction

Contemporary societies have become increasingly complex through accelerating urbanization and technological advancement, making risks not incidental but inherent features of modern life [1]. Public security risk governance thus confronts a fundamental paradox: while effective governance demands proactive, pre-emptive intervention, institutional mechanisms remain predominantly reactive. Current paradigms emphasize policy refinement and technological solutions for emergency response and recovery. While necessary, this macro-level, techno-institutional approach systematically neglects the critical source of risk behaviors: individuals’ psychological decision-making processes within specific contexts. This institutional blindness creates a governance performance gap—organizational systems address symptoms while root causes persist. Bridging this gap fundamentally requires understanding the micro-behavioral mechanisms through which individuals assess and respond to risk.

Addressing this gap requires a fundamental re-examination of risk governance frameworks. Specifically, the analytical focus must shift to the micro-foundational causal chain: from cognitive appraisal through psychological decision-making to behavioral choice to risk outcomes. As Ortwin Renn has posited, risk is fundamentally a product of human creation and choice. Consequently, pathways to risk mitigation must be grounded in the underlying mechanisms of human behavior. This critical theoretical gap calls for an integrative paradigm—one that complements existing macro-level systems by revealing the psychological processes through which individuals construct risk assessments and make behavioral choices under uncertainty.

Human behavior is not arbitrary; it emerges from individuals’ self-organizing capacity as they navigate specific temporal and spatial contexts (T & P), constantly weighing their perceived agency against perceived threats. This psychological calculus—the dynamic interplay between Perceived Control (C) and Perceived Destructive Power (R)—constitutes the critical yet understudied mechanism underlying behavioral choice. Deciphering this mechanism promises a fundamental paradigm shift: from reactive, risk-centered governance to proactive, behavior-centered intervention. By revealing the behavioral sources of risk, it provides a strategic leverage point for designing targeted interventions and promoting systematic risk prevention.

This paper responds to this imperative by examining how individual decision-making shapes risk governance outcomes. We focus on individuals facing contingent, often latent risks—a ubiquitous experience in complex systems exemplified by automotive driving decisions. Our central research question is: How do individuals’ psychological assessments of Perceived Control (C) and Perceived Destructive Power (R) within specific contexts (T, P) shape their behavioral choices, and how can understanding this process enhance the efficacy of risk governance?

To address the gap identified above, this paper develops the F(T, P, C, R) framework—an integrative model that synthesizes self-organization theory with a psychological game perspective to illuminate the micro-mechanisms of individual risk behavior. The remainder of the paper is structured to elaborate this framework: we first synthesize relevant literature to ground its core constructs, formally present the model, and demonstrate its application through case analysis in automotive driving. We then derive its theoretical and practical implications, and conclude by outlining a trajectory for future computational translation via agent-based modeling.

2. Literature Review

A comprehensive understanding of contemporary public security risk governance must draw insights from multiple rapidly evolving disciplines. This paper synthesizes cutting-edge scholarship across four critical domains: (1) the evolving conception of systemic risk and governance frameworks, (2) behavioral science: from decision biases to policy tools, (3) risk perception and behavior in specific contexts—with automotive systems as a central case study, and (4) self-organization and complex systems theory. Through systematic examination of this intellectual landscape, with particular attention to developments since 2019, this paper identifies a shared theoretical imperative and a critical gap in existing literature: the urgent need for an integrative analytical framework capable of dynamically linking micro-level psychological mechanisms to macro-level system dynamics.

2.1. The Evolving Conception of Systemic Risk and Governance

The conceptualization of risk has undergone a fundamental shift: from static, probabilistic calculations to a dynamic understanding of systemic risk characterized by complexity, interdependencies, and nonlinear dynamics [2]. Contemporary scholarship increasingly recognizes that effective risk governance must transcend narrow focus on objective hazards, instead encompassing the cognitive frameworks through which risks are interpreted and the institutional and social mechanisms through which uncertainty is collectively amplified or attenuated. This dual imperative—simultaneously addressing interconnected systemic vulnerabilities while counteracting cognitive distortions that compromise both lay and expert decision-making [3]—reflects an underlying theoretical consensus. This perspective is grounded in the constructivist conception of risk as inherently socially mediated, contingent upon interaction processes and value systems [4,5]. Consequently, contemporary governance architectures must synthesize objective risk assessment methodologies with rigorous attention to subjective meaning-making and the emergent properties of social systems.

2.2. Behavioral Science: From Decision Biases to Policy Tools

Research on human judgment under uncertainty has fundamentally transcended classical rational-actor paradigms. Contemporary scholarship on “noise” and systematic biases in professional decision-making reveals critical limitations of unaided human cognition, thereby establishing the evidentiary foundation for behavioral insights [6]. This theoretical advancement has catalyzed the emergence of behavioral public policy—an approach that strategically translates psychological principles into evidence-based policy interventions. The proliferation of governmental nudge units and behavioral insights teams globally attests to this paradigmatic shift [7]. Systematic meta-analyses have rigorously assessed both the efficacy and ethical implications of behavioral policy tools, substantially strengthening the empirical basis for their deployment across domains including public health and public safety [8]. Nevertheless, while this research corpus substantiates the analytical value of examining choice micro-foundations, its dominant focus on discrete behavioral mechanisms often overlooks the necessity for integrating these insights into coherent, systems-level frameworks of adaptive decision-making.

2.3. Risk Perception and Behavior in Context: The Case of Automotive Systems

Note: In this section, we review literature employing the general terms “perceive control” and “perceived destructive power”. These broad constructs correspond conceptually to the more precise operational definitions of Perceived Control (C) and Perceived Destructive Power (R) that are central to our proposed F(T, P, C, R) model, as formally defined in Section 3.1.

The interplay between risk perception and behavioral outcomes is exemplified prominently in automotive transportation, a complex sociotechnical system. Empirical evidence indicates that driver behavior operates as a dynamic response to perceived risk modulated by multiple factors including prior driving experience, affective states (particularly worry), and confidence in technological systems. It is established, for instance, that risk perception and worry jointly predict driving style [9]. The introduction of autonomous driving (AD) technologies further complicates this psychological framework, positioning trust in automation as a crucial mediating variable linking perceived risk to technology adoption intentions [10]. Investigations into user resistance toward AD systems reveal a systematic interaction between perceived control (C) and perceived risk (R): resistance typically emerges when individuals experience limited perceived control over system operations concurrent with elevated perceived risk of system failure [11,12]. This automotive context thus provides a particularly apt empirical setting for investigating the dynamic interplay between perceived control (C) and perceived risk (R) that forms the theoretical foundation of our proposed model [13].

2.4. Self-Organization and Complex Adaptive Systems in Social Domains

Self-organization theory explains how order and adaptive structure emerge in complex systems from decentralized local interactions [14]. This theoretical framework has gained considerable traction across social science disciplines. Heylighen synthesizes complexity thinking across socio-ecological systems, with particular emphasis on adaptive processes and evolutionary mechanisms [15]. Empirical applications exemplify the theory’s utility in designing disaster management information systems that enable adaptive, coordinated self-organization across distributed actor networks [16]. Methodologically, Agent-Based Modeling (ABM) has become indispensable for formalizing and simulating emergent phenomena in complex social systems [17,18,19]. This computational approach enables systematic investigation of how macro-level dynamics—such as traffic congestion and information cascades—arise from distributed micro-level interactions and behavioral rules. This integrated theoretical and methodological framework constitutes an essential foundation for the paradigmatic shift from static risk models to dynamic, systems-level approaches torisk governance.

2.5. Synthesis and Identified Gap: The Imperative for an Integrative Micro-Macro Model

The literature reveals two robust yet frequently parallel strands of contemporary scholarship. Behavioral science offers granular insights into the cognitive and perceptual micro-mechanisms that drive individual risk decisions (e.g., biases, trust, perceived control). Meanwhile, complex systems science provides powerful frameworks for understanding the emergent, macro-level dynamics and resilience of socio-technical systems. A critical and persistent gap exists in conceptual models that explicitly and dynamically link these two levels of analysis. Specifically, there is a lack of models capable of translating the continuous psychological games of individuals—characterized by factors such as perceived control (C) and risk (R)—into systemic risk governance outcomes, while accounting for the feedback between levels. Current approaches tend to privilege one perspective, yielding either system models devoid of psychological realism or behavioral interventions that overlook systemic complexity and adaptation. We argue that self-organization theory supplies the essential conceptual linkage, as it inherently connects agent-level adaptation (the psychological game) with system-level emergence. The F(T, P, C, R) model developed in the following section is designed to address this gap. By placing the self-organizing, psychologically-engaged individual at the core of systemic risk analysis, it offers an integrative, dynamic framework that directly responds to calls for governance models capable of bridging the cognitive and systemic dimensions of risk [3].

3. Constructing an Individual Psychological Game Model Under Probabilistic Risk

Building upon the identified need for an integrative micro-macro framework, this section establishes the theoretical underpinnings of our core constructs and formally presents the F(T, P, C, R) model. We begin by tracing the theoretical provenance of each constituent element, thereby grounding the framework in established theory and transitioning it from a novel proposition to a theory-informed conceptual model. Subsequently, we articulate the specific research questions the model is designed to address and present its formal structure.

3.1. Theoretical Provenance of the Core Constructs

The four core factors—Time (T), Place (P), Perceived Control (C), and Perceived Destructive Power (R)—are not ad hoc selections but are grounded in established psychological and sociological theories. Their integration constitutes the novel foundation of our behavioral model.

Time (T) & Place (P) as the Situational “Action Space”. The factors T and P collectively define the situational context framing any risk decision. This construct is grounded in Gibson’s Theory of Affordances, which posits that environments are perceived not as neutral backdrops but as action possibilities and constraints [20]. Contemporary environmental psychology further elaborates that places are experienced through the behaviors they enable or inhibit [21]. In our model, T and P thus constitute the perceived “Action Space”—the objective yet subjectively filtered context within which the psychological game unfolds. For instance, the same driving decision (e.g., overtaking) is framed radically differently by temporal factors (day vs. night, rushed vs. leisurely) and spatial factors (a wide highway vs. a narrow urban street).

Perceived Control (C) as Situated Counterforce. The sense of agency an individual mobilizes in a risk situation is conceptualized as the Perceived Control (C). It refers to the perceived force an individual assesses as available to counteract or manage a specific risk. This construct is directly anchored in Bandura’s concept of self-efficacy—the belief in one’s capabilities to organize and execute courses of action required to manage prospective situations [22]. We intentionally differentiate C from related concepts: it is distinct from a generalized locus of control and more situated and behavioral than the often attitudinal perceived behavioral control in the Theory of Planned Behavior. In our model, C is the task-specific, in-the-moment appraisal of one’s capacity to exert a mitigating force against a particular threat, such as a driver’s confidence in handling a skid or a user’s trust in their ability to override an autonomous system.

Perceived Destructive Power (R) as Affective Threat Force. The magnitude of negative consequences an individual attributes to a risk is captured by R. It represents the perceived inherent force of the risk to cause harm. This construct is rooted in the psychometric paradigm of risk perception pioneered by Slovic, which identifies affective dimensions like “dread” as central to judgment [23]. Modern extensions, such as the Social Amplification of Risk Framework, illuminate how social and media processes intensify these perceptions [24]. In our model, R primarily encapsulates the affective, intuitive appraisal of a threat’s severity—the “gut feeling” of its damaging potential. This affective component exerts more immediate behavioral influence than a cold, statistical probability, explaining why vividly perceived risks (e.g., a graphic crash visualization) disproportionately shape decisions.

Synthesizing C and R: The Core Psychological Game. The dynamic interplay between C and R constitutes the engine of the model. While C is grounded in self-efficacy, it also encompasses the related concept of risk tolerance—an individual’s subjective capacity to bear uncertainty [25]. This synthesis is particularly salient in dynamic, cross-cultural contexts where perceptions of control and loss intertwine [26]. The psychological game at the heart of our model thus represents a continuous process: evaluating whether the Perceived Control (C)suffices to match or overcome the Perceived Destructive Power (R) within a given action space (T, P).

3.2. Research Questions

Guided by the theoretical foundations established above and the persistent micro-macro gap identified in the literature, this study is designed to systematically advance the understanding of risk decision-making through three sequential, theory-driven research questions. These questions move from deconstructing the model’s architecture, to analyzing its internal dynamics, and finally to deriving its practical utility.

RQ1 (Architectural): What are the core constituent elements of a self-organization-based model of individual risk decision-making, and what theoretical principles govern their proposed interrelationships?

RQ2 (Mechanistic): How does the dynamic interaction between perceived control (C) and perceived destructive power (R)—conceptualized as a situated psychological game—generate distinct, predictable patterns of behavioral outcomes (e.g., from confirmation to denial)?

RQ3 (Translational): What actionable principles for the design and evaluation of public security risk governance interventions can be derived from the dynamic properties (e.g., conservation, radicalization) of this model?

The F(T, P, C, R) framework presented in Section 3.3 constitutes the direct response to RQ1. The core C–R dynamic of this framework is operationalized in Section 4.3.2 (through the Psychological Game Space and Dynamic Risk-Decision Matrix), and its behavioral implications are further analyzed in Section 5, thereby addressing RQ2. The actionable principles for intervention design derived from the model’s dynamic properties (Section 5.3) provide the direct response to RQ3.

3.3. The Integrated F(T, P, C, R) Model

We therefore posit that an individual’s behavioral choice in a risk-laden situation emerges from a dynamic psychological game, wherein the Perceived Control (C) is continuously compared against the Perceived Destructive Power (R) within a specific time-place action space (T, P). This central proposition is formally expressed as the following conceptual equation:

$$\begin{array}{c}\mathrm{F}\_\mathrm{behavior}=\mathrm{f}(\mathrm{T},\mathrm{P},\mathrm{C},\mathrm{R})\end{array}$$

(1)

where

F_behavior represents the resultant behavioral choice or tendency (e.g., to brake, to adopt a technology, to evacuate).

T and P constitute the objective yet subjectively perceived situational matrix (the action space).

C is the subjective, cognitive-affective assessment of one’s control force/power over the risk.

R is the subjective, affective-cognitive assessment of the risk’s destructive force/power.

The model posits a dynamic, interactive, and non-linear relationship among the factors. A profound deficiency in one factor (e.g., an extremely high R, signifying terror) cannot be easily compensated for by strengths in others (e.g., high C). This non-linearity is essential for capturing the complex, often discontinuous nature of risk decision-making. The internal psychological game fundamentally involves the continuous dynamic comparison and recalibration of C against R, constrained and enabled by the situational frame of T and P. The following section details the methodology for operationalizing and applying this model.

4. Methodological Approach: Theory Synthesis and Model Development

Building upon the identified need for an integrative micro-macro framework (Section 2.5) and the specific research questions derived in Section 3.2, this section details the methodological pathway through which the F(T, P, C, R) conceptual model was systematically constructed, explicated, and its scope defined. We first position the study within the paradigm of theory synthesis, then describe a structured four-step development process grounded in abductive reasoning, and finally delineate the scope of this conceptual model and its implications for subsequent research.

4.1. Methodological Positioning: A Theory-Synthesis Approach

Given the conceptual and integrative nature of our research aim, a qualitative theory-building approach is most appropriate. This study is designed as a conceptual article and employs theory synthesis as its core methodology [27]. This method involves integrating constructs, assumptions, and relational propositions from distinct but complementary theoretical domains to create a novel, coherent conceptual framework. It thereby allows us to bridge the disciplinary gaps identified in the literature review by deliberately combining insights from social cognitive theory, psychometric risk research, and complex systems theory into the unified F(T, P, C, R) model.

4.2. Research Procedure: A Four-Step Model Development Process

The development and elaboration of the F(T, P, C, R) model followed a structured, four-step iterative process grounded in abductive reasoning and theory synthesis. This procedure operationalizes our methodological stance and is explicitly designed to address the research questions outlined in Section 3.2.

Step 1: Abductive Inference from a Practical Governance Puzzle. The inquiry originated from a persistent observation: why do sophisticated top-down risk policies and technologies often fail to achieve desired behavioral compliance? From this practical puzzle, we formulated the abductive inference that a central gap in understanding might exist. Specifically, we identified the need to decode the micro-foundational mechanisms, particularly the internal psychological calculus governing individual decisions in risk-laden situations.

Step 2: Interdisciplinary Synthesis and Construct Identification. Guided by this inference, we conducted a targeted, interdisciplinary literature review (Section 2) to identify and integrate relevant theoretical lenses. This synthesis led to the selection and precise theoretical grounding of our four core constructs—Time (T), Place (P), Perceived Control (C), and Perceived Destructive Power (R)—as detailed in Section 3.1. The meta-theory of self-organization emerged as the pivotal concept capable of unifying these constructs into a dynamic, process-oriented model.

Step 3: Model Formalization and Visual Representation. The synthesized theoretical components were then translated into a formal conceptual model. This involved: (a) expressing the core proposition as the functional equation F_behavior = f(T, P, C, R); (b) deriving the core dynamic principles (Conservation and Radicalization) from the interplay of C and R; (c) developing the diagnostic Dynamic Risk-Decision Matrix (

Table 1. Dynamic Risk-Decision Matrix: Classification of Behavioral Outcomes Based on the C-R Game.

Zone	Perceived Control (C)	Perceived Destructive Power (R)	Behavioral Tendency	Typical Cognitive & Emotional State
① Confirm	High	Low	Decisive Execution. Swift, confident engagement in the risky behavior.	Overconfidence/Complacency. Possible underestimation of actual risk, stemming from high trust in one’s own control.
② Tend-to-Confirm	Moderately High	Moderately Low	Inclination to Execute. After deliberation, more likely to engage, but retains caution.	Cautious Optimism. Engages in cost–benefit analysis; responsive to “nudges” that enhance C or reduce R.
③ Tend-to-Deny	Moderately Low	Moderately High	Inclination to Avoid. Leans towards abandoning the risky behavior amidst anxiety; decision-making is slow.	Anxiety/Hesitation. High alertness to threat but low perceived coping efficacy; prone to “decision paralysis.”
④ Deny	Low	High	Decisive Rejection. Swift, resolute avoidance of the risky behavior.	Fear/Avoidance. Considers the risk uncontrollable and its consequences unacceptable; opts for complete withdrawal.

Note: C = Perceived Control; R = Perceived Destructive Power. See Figure 2 for a detailed visualization of the Psychological Game Space and the dynamic “Risk-Safety” Boundary.

); and (d) creating complementary visual frameworks (Figure 1 and Figure 2) to illustrate the decision process and the psychological game space.

The diagram depicts a four-stage adaptive loop (defined in Section 4.3.1) with arrows indicating the functional transitions between stages: ① Situational Input → Psychological Gaming, ② Psychological Gaming → Behavioral Output, ③ Behavioral Output → Feedback Loop, ④ Feedback → Modulation (see Section 4.3.1 for full details).

Note: The solid arrow illustrates the Conservation principle (gradual adaptation toward safety); the dashed arrow illustrates the Radicalization principle (rapid reversal toward risk aversion). The diagonal line (C = R) represents the dynamic “Risk-Safety” Psychological Boundary. Decisions in Zones ① and ② (where C > R) are experienced as “safe,” leading to behavioral confirmation. Decisions in Zones ③ and ④ (where C < R) are experienced as “dangerous,” leading to denial. This boundary can shift with changes in self-organizing capacity or situational re-evaluation.

Zone classifications:

Zone ①: Confirm (High C, Low R)

Zone ②: Tend-to-Confirm (Moderately High C, Moderately Low R)

Zone ③: Tend-to-Deny (Moderately Low C, Moderately High R)

Zone ④: Deny (Low C, High R)

Step 4: Theoretical Elaboration through Analytical Casing. To demonstrate the model’s internal coherence and explanatory plausibility, we employed analytical casing. This method involves the detailed, thought-experimental application of the model to concrete scenarios to animate its logic and operationalize its constructs. For this study, we selected automotive driving behavior as the primary analytical context. We constructed two detailed narrative cases: a simple, instantaneous decision (e.g., responding to a yellow light) and a complex, protracted decision (e.g., adopting autonomous driving features). These cases serve not as statistical samples but as vehicles for explicating the model and demonstrating its capacity to dynamically trace the cognitive and behavioral journey of a decision-maker, thereby illustrating the model’s internal logic and explanatory scope.

As a conceptual theory-building study, this framework aims to provide a diagnostic lens and vocabulary for analysis, with its parameters and functional relationships awaiting empirical calibration in future research.

4.3. Model Exposition and Case Analysis

Building on the methodological foundations established in Section 4, this section advances the theoretical exposition of the F(T, P, C, R) model and demonstrates its analytical power. The exposition centers on the model’s four constitutive elements: the situational context of Time (T) and Place (P), and the cognitive assessments of Perceived Control (C) and Perceived Destructive Power (R). We first provide a consolidated explication of these four core elements and their dynamic interplay. This theoretical groundwork enables the construction of the Psychological Game Space (Figure 2), from which we derive the governing dynamic principles of Conservation and Radicalization and operationalize the framework into the diagnostic Dynamic Risk-Decision Matrix (Table 1). The section culminates in a dual-case study within driving, designed to validate the model’s utility in diagnosing both split-second decisions and protracted behavioral evolution.

4.3.1. Explication of Core Elements: The Situational Frame and Cognitive Game

Central to our model is the proposition that an individual’s behavioral choice in a risk-laden situation emerges from the interaction of two coupled systems: a Situational Frame (constituted by Time [T] and Place [P]) and a Cognitive Game (driven by the dynamic interplay between Perceived Control [C] and Perceived Destructive Power [R]).

The Situational Frame (T & P): The “Action Space”. Time (T) and Place (P) jointly constitute the objective yet subjectively perceived context for decision-making. T encompasses both chronology and time pressure (e.g., urgency, time window). P refers to the physical and social environment with its inherent affordances and constraints (e.g., road geometry, visibility, social norms). Collectively, T and P define the action space—the contextual container within which the psychological game is situated and its parameters are set.

The Cognitive Game (C & R): The Core Comparative Process. Within this situational frame, the decision-maker engages in a continuous psychological game centered on two concurrent subjective assessments:

Perceived Control (C): The belief in one’s ability to manage or avoid a specific risk (“Can I handle this?”). Rooted in self-efficacy theory, C is shaped by skills, experience, resources, and trust in technology.

Perceived Destructive Power (R): The intuitive, affective sense of the severity of potential harm (“How bad could this be?”). Grounded in psychometric risk perception research, R is driven by the vividness and emotional resonance of the potential negative outcome.

The Core Process: The model conceptualizes risk decision-making as a dynamic, moment-to-moment comparison between C and R, evaluating whether perceived control is sufficient to counter the perceived risk. This continuous calibration process, embedded within the T-P frame, is schematized as a four-stage, self-organizing psychological loop (see Figure 1).

The four stages of this loop are:

(1) Situational Input: The perception of the Time (T) and Place (P) context, which defines the initial Action Space.

(2) Psychological Gaming: The core cognitive-affective process where Perceived Control (C) is dynamically compared against Perceived Destructive Power (R).

(3) Behavioral Output: The resulting behavioral tendency (Confirm/Engage or Deny/Avoid) generated from the C-R evaluation.

(4) Feedback Loop: The assimilation of behavioral outcomes, leading to updates in the perceptions of C, R, and the situational frame, thereby enabling learning and adaptation.

As illustrated in Figure 1, the functional transitions between these stages are:

Arrow ① (from (1) to (2)): Situational Input to Psychological Gaming. This transition converts the perceived situational input (Time and Place, T/P) into an internal cognitive-affective evaluation weighing Perceived Control against Perceived Destructive Power (C vs. R).

Arrow ② (from (2) to (3)): Psychological Gaming to Behavioral Output. This transition translates the outcome of the C–R evaluation into a concrete behavioral tendency: either to Confirm/Engage with or to Deny/Avoid the risky situation.

Arrow ③ (from (3) to (4)): Behavioral Output to Feedback Loop. This transition channels the consequences of the action back into the individual’s cognitive system, providing the basis for learning and adaptive updating.

Arrow ④ (from (4) to (1) & (2)): Feedback to Modulation. This transition completes the cycle by using the updated knowledge to retroactively modulate (1) the future interpretation of Situational Input and (2) the parameters of subsequent Psychological Gaming, thereby closing and sustaining the self-organizing process.

4.3.2. The Psychological Game Space, Dynamic Principles and the Decision Matrix

To capture the dynamic, non-linear nature of the C–R psychological game and render it diagnostically actionable, we present an integrated suite of analytical constructs: the Psychological Game Space (Figure 2), its governing dynamic principles, and their operational counterpart, the Dynamic Risk-Decision Matrix (Table 1).

(1)

The Psychological Game Space

This space defines an individual’s risk decision state by Perceived Control (C, horizontal axis) and Perceived Destructive Power (R, vertical axis). The diagonal “Risk-Safety” Psychological Boundary (where C = R) separates safe zones (Zones ① & ②, where C > R) from dangerous zones (Zones ③ & ④, where C < R). The opposing arrows in Figure 2 encapsulate the model’s core asymmetric dynamics: Conservation (solid arrow) and Radicalization (dashed arrow). (See Figure 2 and its caption for a detailed exposition of these dynamics and the definition of each zone).

By mapping the continuous C–R comparison onto a two-dimensional coordinate system (with Perceived Control [C] on the horizontal axis and Perceived Destructive Power [R] on the vertical axis), the temporal flow of psychological gaming is transformed into a spatially structured, dynamically interpretable landscape. Figure 2 depicts this Psychological Game Space.

An individual’s position within this space is dynamic, evolving through variations in self-organizing capacity. This capacity, driven by experiential learning, informational updates, and changes in the Situational Frame (T, P), primarily modulates the balance between C and R.

When self-organizing capacity strengthens, it typically enhances C and/or attenuates R, propelling the decision point on a trajectory from higher-risk toward lower-risk zones (e.g., from Zone ④, through ③ and ②, toward ①). Conversely, a weakening of this capacity diminishes C and/or amplifies R, triggering a regression from lower-risk toward higher-risk zones (e.g., from Zone ①, through ② and ③, toward ④). The opposing arrows in Figure 2 encapsulate this core bidirectional dynamic. They embody the self-organizing feedback loop at the heart of the model and directly illustrate how the continuous calibration of self-organizing capacity governs the mechanism of behavioral outcome generation (RQ2).

(2)

Core Dynamic Principles: Conservation and Radicalization

The movement of an individual’s decision point within the Psychological Game Space is governed by two fundamental, asymmetric dynamic principles derived from the F(T, P, C, R) model: Conservation and Radicalization. These principles explain the non-linear and often discontinuous nature of behavioral change in risk contexts, and are visually encapsulated by the opposing directional arrows in Figure 2.

Conservation in Safe Zones (Zones ① & ②): This principle describes the inertia of perceived safety. When C meets or exceeds R, the system exhibits a strong tendency to maintain this equilibrium. Positive feedback from successful experiences reinforces the existing C–R balance, creating cognitive and behavioral inertia. This explains the significant challenge in proactive risk governance: persuading individuals who feel safe (e.g., complacent drivers) to adopt even safer behaviors requires overcoming this inherent resistance. In Figure 1, Conservation corresponds to the gradual, effortful rightward/downward trajectory depicted by the solid arrow.

Radicalization across the Risk-Safety Boundary (Transition into Zones ③ & ④): This principle captures the accelerated, non-linear shift toward risk aversion that occurs when R overtakes C, causing the decision point to cross the psychological boundary. This shift is rapid and disproportionate, mirroring ‘critical fluctuations’ in complex systems [28]. A single salient negative event (e.g., a near-miss) can trigger a sharp spike in R, leading to a decisive behavioral reversal. In Figure 1, Radicalization is depicted by the rapid, discontinuous leftward/upward trajectory of the dashed arrow.

The interplay between Conservation and Radicalization introduces a critical asymmetry into the decision landscape, as visualized by the opposing arrows in Figure 2: safety is accumulated through a slow, cumulative process, yet can be dissipated almost instantaneously. It should be emphasized that while the initial positioning of an individual’s decision point in the Psychological Game Space arises from a confluence of stochastic factors—including contextual variables such as T and P—and while its subsequent trajectory may exhibit nonlinear and contingent shifts, the underlying directionality of its movement is governed by a determinate internal logic. This logic is fundamentally tied to the individual’s capacity for self-organization. Specifically, when this capacity strengthens, it drives the decision point along a path of progressive risk mitigation, moving from Zone ④ toward Zone ①, as represented by the solid arrow. Conversely, a weakening of self-organizing capacity propels the decision point along a path of progressive risk radicalization, following the trajectory of the dashed arrow. Thus, the core intent of this study is to systematically examine this intrinsic linkage—between an individual’s self-organizing capacity and the dynamic evolution of their risk decision-making and behavioral expressions.

(3)

The Dynamic Risk-Decision Matrix

To translate the dynamic Psychological Game Space into a practical diagnostic and intervention-oriented tool, we derive the Dynamic Risk-Decision Matrix (Table 1). This matrix systematically classifies the cognitive-emotional profile and behavioral tendency corresponding to each of the four Zones in Figure 2, thereby creating a structured taxonomy for behavioral analysis and targeted governance design.

The matrix and the game space are isomorphic representations: the former provides a categorical taxonomy for diagnosis, while the latter offers a dynamic spatial representation of the decision process. Together, they bridge the conceptual F(T, P, C, R) model with practical diagnostics.

4.3.3. Dual-Case Exposition in the Driving Context

Building on the analytical casing approach established in Section 4.2 and equipped with the integrated analytical tools developed above—the Psychological Game Space (Figure 2), its core dynamic principles (Conservation and Radicalization), and the Dynamic Risk-Decision Matrix (Table 1)—we apply the F(T, P, C, R) framework to two concrete driving scenarios to demonstrate its diagnostic power across both instantaneous decisions and longitudinal behavioral adaptation.

(1)

An Instantaneous Decision: The Yellow Light Dilemma

Scenario: A driver approaches an intersection as the traffic signal turns yellow, necessitating a rapid choice: accelerate to cross or decelerate to stop.

Model Application: Mapping the Scenario onto the F(T, P, C, R) Variables. We apply the framework by specifying the four factors for this scenario:

T (Time): The objective reaction window (determined by speed and distance to the stop line) and the subjective sense of urgency (e.g., feeling rushed).

P (Place): The physical and social context, including road conditions (dry/wet), intersection geometry, visibility, and the presence of other road users.

C (Perceived Control): The driver’s instantaneous self-assessment of their capability to execute either maneuver safely, drawing upon vehicle handling skills and past experience.

R (Perceived Destructive Power): The intuitive, affective weight given to potential negative outcomes, ranging from the fear of a collision to the anticipated social or legal consequences.

Diagnostic Tracing:

Baseline State: Given a context where the driver perceives high C (e.g., high confidence) and low R (e.g., an empty intersection with minor perceived consequences), the model situates this cognitive state within Zone ① (Confirm). The corresponding behavioral tendency is decisive action (e.g., accelerating to cross).

Dynamic Shift: Now, introduce a change in the situational frame (P): a pedestrian steps onto the crosswalk. This alters the driver’s assessment, causing a sharp increase in R (heightened fear of collision). The updated input (high C, now-high R) repositions the cognitive state within Zone ③ (Tend-to-Deny). The predicted behavioral output shifts accordingly, from confirmation to avoidance (e.g., emergency braking).

Case Summary: This case demonstrates the procedural application of the F(T, P, C, R) framework for diagnosing a split-second risk decision. It shows how changes in the situational frame (P) can be mapped onto shifts in perceived risk (R), leading to a recalibration of the C–R balance and a consequent change in the diagnosed behavioral tendency within the matrix.

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An Evolving Decision: The Adoption of Autonomous Driving Features

Scenario: We trace an experienced driver‘s stance toward adopting an “Autopilot” feature over time.

Model Application: Mapping the Scenario onto the F(T, P, C, R) Variables. We apply the framework by tracking the evolution of the four factors across key phases:

T (Time): The extended timeframe of adoption, encompassing moments of initial exposure, specific catalytic events, and long-term use.

P (Place): The driving environment, intentionally varied from optimal (e.g., sunny, light traffic) for initial trials to more complex conditions over time.

C (Perceived Control): The driver‘s evolving assessment of their ability to manage or oversee the system safely, ranging from initial distrust to growing trust through experience.

R (Perceived Destructive Power): The driver’s intuitive sense of the potential severity of system failure, shaped by prior information, direct experience, and observed system performance.

The driver’s psychological trajectory maps a path across the Psychological Game Space (Figure 2), embodying the model‘s self-organizing feedback loop.

Diagnostic Tracing:

Phase 1–Initial Stance: The driver perceives very low C (distrust in automation, high confidence in own skill) and very high R (fear of system failure based on available information). This cognitive profile (C < R) is diagnosed within Zone ④ (Deny) on the matrix, corresponding to a behavioral tendency of rejection.

Phase 2–First Exposure Under Optimized Conditions: The driver trials the system under favorable conditions (P). The direct experience slightly lowers R, but C remains low due to unfamiliarity. The new cognitive profile (C < R, but R reduced) moves the diagnosis to Zone ③ (Tend-to-Deny). The predicted behavior is hesitant, supervised use.

Phase 3–Post-Positive Experience: After a salient positive event (e.g., the system effectively assists in a critical situation), the driver’s assessment updates: C increases substantially (trust grows), while R decreases further (learned system boundaries). The new profile (C ≈ R or C > R) moves the diagnosis to Zone ② (Tend-to-Confirm). The predicted behavior is willing, conditional use.

Phase 4–Routine Use: After prolonged positive experience, the cognitive profile stabilizes at high C and low R. This state is diagnosed within Zone ① (Confirm), corresponding to a behavioral tendency of habitual, confident reliance on the system.

Case Summary: This case demonstrates the application of the F(T, P, C, R) framework for diagnosing the evolution of risk perceptions and behavioral tendencies over an extended period. It tracks how sequences of experiences and information update the C–R assessments, leading to a diagnosed migration across different zones of the Dynamic Risk-Decision Matrix.

5. Theoretical Analysis and Practical Implications

Having established the F(T, P, C, R) framework and demonstrated its explanatory power through concrete scenarios, we now turn to a deeper synthesis. This section moves from exposition to interpretation and integration. We first leverage the Dynamic Risk-Decision Matrix to diagnose the cognitive-affective substrates of behavioral outcomes (Section 5.1). We then critically position our model within the broader theoretical conversation, clarifying how it extends existing paradigms (Section 5.2). This theoretical integration yields a set of actionable, zone-specific principles for intervention design, addressing a core practical challenge in risk governance (Section 5.3).

5.1. The Matrix as a Diagnostic Lens: From Behavior to Underlying Cognitive-Affective States

The four-zone Dynamic Risk-Decision Matrix (Table 1) provides the lens for a diagnostic analysis, exposing the distinct psychological architectures that constitute each zone and drive observable behavior. Synthesizing insights from the preceding cases, this section defines the constitutive logic governing these zones.

Zone ① (Confirm): The Architecture of Complacent Confidence. Here, high perceived control (C) decisively overshadows low perceived destructive power (R). This equilibrium fosters a potent optimism bias and systematic risk underestimation. Crucially, it is governed by the Conservation principle identified in our model. Positive feedback from routine success reinforces this C-R balance, creating a self-sustaining inertia resistant to external warnings. The longitudinal case illustrated how prolonged positive experience can solidify this state, potentially leading to over-trust. The governance challenge here is not merely informing individuals of risk, but disruptively recalibrating a psychologically comfortable state of perceived safety.

Zone ② (Tend-to-Confirm): The Landscape of Cautious Optimism. Occupants of this zone engage in active deliberation, holding a favorable yet precarious C-R balance. As seen in the autonomous driving case’s third phase, individuals here are receptive to contextual shifts and evidence. This makes them the primary target for nudge-based interventions (e.g., setting safer defaults, simplifying choices, providing social proof). Small, strategic adjustments to C or R can tip the scale toward consistent safe behavior.

Zone ③ (Tend-to-Deny): The Dynamics of Anxious Deliberation. Characterized by high R and low C, this zone is marked by decision paralysis and hyper-vigilance—akin to the driver’s state after noticing a pedestrian. It is highly sensitive to the Radicalization principle. Minor negative cues can trigger disproportionate shifts toward complete avoidance, as the yellow-light case showed. Interventions here require extreme care: strategies that amplify R (e.g., fear appeals) are counterproductive, while those that build C through mastery experiences, clear protocols, or guaranteed support are essential to prevent a collapse into Zone ④.

Zone ④ (Deny): The Logic of Resigned Avoidance. When R is perceived as overwhelmingly high and C as negligible, complete disengagement is the perceived only rational outcome. Moving individuals out of this zone—as the initial state of the autonomous driving skeptic—requires more than information. It demands systemic trust-building and agency restoration through participatory design, phased implementation with clear opt-outs, and credible social proof to initiate the arduous climb toward considering action.

In summary, the matrix translates behavioral observations into a diagnosis of underlying cognitive-emotional configurations. It reveals that behavioral change is a non-linear migration across psychological landscapes, driven by updates to C and R. Understanding these state-specific logics is the prerequisite for effective intervention, a theme expanded in Section 5.3.

5.2. Theoretical Integration: Positioning the F(T, P, C, R) Model

The validity and utility of a novel conceptual model are established through its dialogue with and contribution to the broader theoretical landscape. Building on the psychological dynamics diagnosed in the preceding Section 4, this section positions the F(T, P, C, R) framework by engaging in a critical comparative analysis with two cornerstone theories of behavioral decision-making. It articulates how our model extends their explanatory scope to encompass the dynamic, context-embedded, and non-linear risk choices that characterize complex systems.

Advancing beyond Static Antecedents: From Predicting Intention to Modeling the Dynamic “Moment of Choice”. The Theory of Planned Behavior (TPB) accounts for behavioral intention via attitudes, norms, and perceived behavioral control (PBC). While PBC shares ground with our Perceived Control (C), TPB treats it as a relatively stable antecedent. Our framework introduces a critical shift toward dynamic, situated recalibration. Here, C is a fluid, in-the-moment sense of agency, perpetually weighed against a contemporaneous assessment of threat (R) within a specific T–P action space. This refocuses the lens from predicting intention to modeling the real-time process of decision-making, thereby explaining the behavioral volatility and sudden reversals illustrated in our cases. It thereby provides a granular mechanism for the behavioral volatility and sudden reversals illustrated in our cases—such as the driver’s instantaneous shift from crossing to braking—phenomena less readily captured by a model of static predictors.

From Linear Mediation to a Mappable, Interactive Decision Space. Protection Motivation Theory (PMT) offers a closer parallel, with its Threat Appraisal (severity + vulnerability) and Coping Appraisal (self-efficacy + response efficacy) closely mapping onto our R and C constructs, respectively. Our contribution lies in formalizing and spatializing their dynamic interplay. First, we move beyond modeling these appraisals as mediators in a linear process; instead, we spatialize the cognitive-emotional calculus within the Psychological Game Space (C–R Space). This transforms an internal mediating process into an analysable decision landscape where the relative magnitude of C and R, not just their independent scores, determines outcome. Second, we explicitly elevate the situational context (T, P) from a moderating variable to a constitutive element of the decision function, defining the very “arena” in which the C–R game is played. Third, and most significantly, we derive the governing dynamic principles of Conservation and Radicalization from this interaction. These principles allow us to predict not just whether a protective behavior is likely, but to characterize the stability of that tendency and its potential for non-linear collapse or transformation—explaining both the inertia of the complacent driver and the tipping points observed in the adoption journey.

Providing a Plausible Micro-Foundation for Complex Systems Analysis. Beyond enriching behavioral theory, the model’s formal structure serves a crucial bridging function. By conceptualizing the individual as a self-organizing system where macro-level behavior (F_behavior) emerges from the continuous interaction of internal states (C, R) within an environmental frame (T, P), it provides a psychologically plausible “source code” for complex-systems science. The non-linear zone transitions, governed by Conservation and Radicalization, represent micro-level “critical transitions.” This formalization allows the F(T, P, C, R) framework to directly inform Agent-Based Models (ABMs), enabling the simulation of how collective risk phenomena (e.g., epidemic spread of risky behaviors, sudden collapses in public trust) emerge from populations of agents operating under these rules. It thereby directly addresses the micro-macro integration gap identified in our literature review (Section 2.5), offering a generative link between psychological game and systemic governance outcome.

5.3. Practical Implications: A Framework for Targeted Intervention Design

Consequently, the F(T, P, C, R) model translates into a structured logic for designing behavioral and policy interventions [29]. This diagnostic-to-prescriptive logic is essential for overcoming the frequent mismatch between top-down strategies and individual psychological realities [30], moving decisively toward precision behavioral governance.

Governance for Zone ① (Complacent Confidence): Calibrating Overconfidence and Disrupting Inertia. The primary challenge here is overcoming the Conservation principle—the self-reinforcing inertia of perceived safety. Interventions must aim to recalibrate the C–R balance by making risk (R) perceptually salient and personally relevant, while respectfully challenging overconfidence (C). This can be achieved through vivid, experiential risk communication (e.g., immersive simulator demonstrations that render consequences tangible), providing direct feedback on specific skill deficits, or designing regulations and environments that make the negative outcomes of risk-taking more immediate. The goal is not merely to inform, but to disrupt a psychologically comfortable equilibrium.

Governance for Zone ② (Cautious Optimism): Nudging the Balance and Optimizing Choice Architecture. Individuals in this zone are already deliberative and inclined toward safer behavior, making them ideal candidates for choice architecture interventions. The goal is to gently nudge the C–R balance further toward consistent confirmation by reducing friction and leveraging cognitive tendencies. Effective strategies include setting safer options as defaults (e.g., auto-enrollment in safety features), simplifying procedures to reduce cognitive load and effort, and leveraging descriptive social norm messaging (“Most drivers in your situation maintain a safe distance”). These light-touch interventions leverage existing openness while systematically guiding choice.

Governance for Zone ③ (Anxious Deliberation): Scaffolding Action by Building Self-Efficacy. This group is highly threat-aware (high R) but feels incapable of coping (low C), leading to hesitation and sensitivity to Radicalization. Interventions must prioritize building perceived control (C) to counterbalance the salient threat. Mastery experiences—such as hands-on training, guided practice in safe environments, or structured simulations—are crucial. Providing clear, simple, and actionable plans, along with guarantees of support, can empower decisive action. Critically, interventions in this zone must avoid fear-based appeals that amplify R, as they risk triggering a Radicalization shift into complete avoidance (Zone ④).

Governance for Zone ④ (Resigned Avoidance): Systemically Rebuilding Trust and Agency. Here, risk is perceived as overwhelming and uncontrollable (R > C). Moving individuals out of this zone requires deep, structural interventions focused on trust and agency restoration. This necessitates more than information; it involves co-designing solutions with affected communities, conducting transparent and demonstrable safety validations, implementing technologies in phased introductions with clear user control and opt-out options, and leveraging trusted influencers or peer advocates to facilitate re-engagement. The aim is to initiate the slow, gradual process of rebuilding C and rationally calibrating R, reversing the Radicalization trajectory.

In summary, the F(T, P, C, R) framework moves risk governance beyond generic campaigns by enabling a structured, zone-specific logic for intervention design (Section 5.3). This approach, rooted in the framework’s capacity to diagnose the dominant cognitive-affective state of a target group (Section 5.1), allows policymakers to select matched strategies—disrupting inertia, optimizing choice, building efficacy, or rebuilding trust—thereby enhancing efficacy and reducing unintended consequences. Having demonstrated its utility as a diagnostic tool and established its theoretical novelty through integration with existing paradigms (Section 5.2), a full appraisal of this conceptual model now requires acknowledgment of its scope and constraints, as well as a synthesis of its answers to the guiding research questions. The following chapter therefore shifts from analysis to comprehensive synthesis and forward-looking discussion.

6. Synthesis, Conclusion, and Future Trajectory

This concluding chapter synthesizes the core contributions of this study, positions it within the broader scholarly and practical landscape, and charts a coherent trajectory for future work. We begin by integrating a discussion of the framework’s limitations with a synthesis of its key contributions (Section 6.1). We then elaborate on its theoretical and practical valorization (Section 6.2), before concluding with a focused agenda for future research and development (Section 6.3).

6.1. Limitations and Synthesis of Contributions

A balanced appraisal of this conceptual research requires acknowledging its inherent constraints, which in turn provides the context for a clear articulation of its achievements.

6.1.1. Limitations

The most salient limitation stems from the qualitative and propositional nature of the present model. While the F(T, P, C, R) framework identifies core variables and explicates their proposed interrelationships, it does not specify precise functional forms, interaction weights, or quantitative thresholds. The illustrative case studies serve to demonstrate internal coherence and explanatory potential—a “proof of concept”—rather than to provide statistical validation or generalizable evidence. Consequently, the model’s parameters and the strength of its proposed dynamics await empirical calibration across diverse populations, risk domains, and cultural contexts.

6.1.2. Synthesis of the Argument: Answers to the Research Questions

Within the bounds of its conceptual scope, this study makes significant strides in bridging micro-level psychology and macro-level governance. Our synthesis provides direct answers to the research questions that guided this inquiry:

RQ1 (Architectural): We have constructed the F(T, P, C, R) model, theoretically grounding individual risk behavior as an emergent outcome of the dynamic interplay between a situational frame (Time T and Place P) and a continuous cognitive-affective game (Perceived Control C vs. Perceived Destructive Power R).

RQ2 (Mechanistic): Through the Psychological Game Space and the Dynamic Risk-Decision Matrix, we have operationalized the model. This elucidates how specific C–R configurations generate predictable behavioral tendencies (Confirm, Tend-to-Confirm, Tend-to-Deny, Deny) and are governed by the asymmetric dynamics of Conservation (inertia in safe zones) and Radicalization (rapid collapse into risk aversion).

RQ3 (Translational): The analysis yields a structured, diagnostic logic for intervention. By matching governance strategies to the cognitive-behavioral zone of the target audience, the model enables a critical shift from generic campaigns to precision behavioral governance, as detailed in Section 5.3.

6.2. Theoretical and Practical Valorization

The framework’s value extends beyond its immediate explanatory power, serving as an integrative platform for interdisciplinary dialogue and practical application.

Theoretical Valorization: This work establishes a shared conceptual interface. For psychologists, it offers a systems-aware model of dynamic decision-making; for complex systems scientists, it provides a psychologically rich micro-foundation for agent behavior in simulations; and for policy scholars, it delivers a theory-grounded framework for behavioral intervention design. By dynamically integrating constructs from self-organization, social cognition, and risk perception research, the framework advances our understanding of how situational, cognitive, and affective factors jointly shape risky behavior.

Practical Valorization and Broader Applications: In practice, the framework functions as both a diagnostic checklist and a design toolkit. It prompts critical questions when policies fail: Was perceived control (C) misjudged? Did messaging inadvertently amplify dread (R)? The zone-based logic translates these diagnostics into actionable guidance. Furthermore, the model’s structured nature grants it broad applicability. For instance, in cybersecurity, it explains user complacency (Zone ①) toward complex threats; in public health, it maps vaccine hesitancy to specific C-R imbalances, suggesting targeted strategies like mastery experiences for the anxious (Zone ③); in financial decision-making, it explains both inertia in portfolio rebalancing (Zone ②) and panic selling (Zone ④) through the principles of Conservation and Radicalization.

6.3. Future Research Trajectory

This trajectory entails advancing along three interdependent pathways to mature the F(T, P, C, R) framework from a conceptual heuristic into a formal, predictive theory.

To advance the F(T, P, C, R) framework from an explanatory conceptual model toward a predictive, computable, and bounded formal theory, a coordinated and multi-strategy research agenda is essential. The most pivotal step lies in computational formalization, which bridges micro-level psychology and macro-level phenomena. Translating the dynamic rules of the C–R psychological game into an agent-based model represents the crucial pathway for micro-macro integration. Such a model would allow researchers to simulate how heterogeneous agents—with dynamically updated C and R values—generate population-level risk phenomena, such as the diffusion of risky behaviors, S-shaped technology adoption curves, or sudden collapses in public trust. Equally important, it would enable the systematic testing, in silico, of intervention strategies tailored to different cognitive-behavioral zones—e.g., “disruption” in Zone ① or “efficacy-building” in Zone ③—evaluating their long-term effects and potential unintended consequences, while exploring system resilience under varying environmental shocks. This work would provide complex-systems science with a psychologically plausible agent-behavior module ready for integration.

Parallel empirical calibration and hypothesis testing are indispensable for establishing the model’s testability. Developing reliable and valid psychometric instruments to capture the momentary states and individual differences in the four core constructs (T, P, C, R) is a prerequisite for quantitative validation. Subsequently, well-designed experiments and longitudinal field studies can rigorously examine specific model-derived hypotheses—for instance, whether a targeted “mastery experience” intervention reliably increases perceived control (C) and shifts behavior from the “Anxious Deliberation” of Zone ③ toward the “Cautious Optimism” of Zone ②, or whether a salient fear-based appeal triggers the Radicalization dynamic, causing decisions to collapse from Zone ② into Zone ④. Such empirical work aims to supply causal evidence for the framework’s dynamic principles and elevate it from a diagnostic heuristic to a predictive, quantitative model of behavioral change.

Finally, theoretical maturation requires boundary expansion and cross-domain engagement. The current framework offers a clear architecture for incorporating moderating variables such as cultural worldviews, personality traits, and social identity; integrating these would elucidate individual and cultural differences in risk decision-making and enrich the theory’s explanatory layers. More challenging yet intellectually rewarding is the application and stress-testing of the model in domains distinct from the original cases—such as climate-change adaptation, cybersecurity practices, or organizational safety compliance. Cross-domain application not only rigorously assesses external validity but also, by distinguishing universal mechanisms from context-specific conditions, helps define the theory’s boundaries and, in the process, stimulates new theoretical insights that drive further refinement and evolution of the framework itself.

In conclusion, the F(T, P, C, R) framework offers more than a novel lens on risk behavior; it provides a generative and integrative platform. By furnishing a common language that connects cognitive-affective processes to systemic outcomes, it bridges long-standing disciplinary divides between psychology, complex systems science, and policy design. We envision it not as a closed theory, but as an open foundation for interdisciplinary inquiry. Through the collaborative pathways outlined—computational modeling, empirical testing, and applied design—this framework can evolve from a compelling conceptual heuristic into a cornerstone for building more resilient socio-technical systems and enacting more precise, behaviorally informed governance.