From One Cause to Webs of Causality

Abstract

Wicked problems defy simple solutions. From climate change to mass shootings, their causes are not singular but systemic, interconnected, and often politicized. Yet in both public discourse and policy design, one-cause and root-cause thinking continue to dominate. This paper introduces Webs of Causality (WoC) and Connect-the-Dots (CtD) thinking as cognitively grounded, empirically supported frameworks for understanding and addressing wicked problems. Drawing on four complementary studies—including experimental interventions, national surveys, and systematic literature reviews—we demonstrate: (1) the persistent human tendency to select and politicize a single cause from a known WoC, and (2) the effectiveness of six DSRP-based cognitive moves in improving causal reasoning and solution design. Together, these studies validate a new cognitive protocol for mapping complex problems and designing systemic, simultaneous interventions. We argue for a paradigm shift in policy and education—away from partial, politicized solutions and toward comprehensive, coordinated responses that reflect the real-world complexity of the problems we face.

1. Introduction

The 21st century is defined by wicked problems [1,2]—ill-structured, systemic issues that resist definitive solutions. Examples range from global climate crises to local school shootings. What makes them wicked is not just complexity, but our inability to align our mental models with the systemic nature of these problems. In a world increasingly volatile, uncertain, complex, and ambiguous (VUCA), our thinking remains largely linear, anthropocentric, mechanistic, and ordered (LAMO).

Solving wicked problems requires a shift in thinking—a move from one-cause explanations to understanding and acting on Webs of Causality (WoC). These webs represent the interconnected nature of causes that must be addressed simultaneously, not in isolation. This simultaneity applies to causal intervention—not to the problems per se, but to the coordinated targeting of multiple causes that reinforce the problem system. They are interdependent networks of causes that interact systemically and are often seen differently depending on ideological or stakeholder perspectives. Equally important is the practice of Connecting the Dots (CtD), which turns systemic analysis into action. This paper explores how our mental models obstruct or enable systemic solutions and presents empirical data to support cognitive interventions that improve our ability to think in WoC and CtD terms. We define Webs of Causality (WoC) as the complex, interdependent networks of multiple causes that interact to produce wicked problems—problems that cannot be solved by isolating a single root cause. Unlike traditional models that assume linear or hierarchical causality, WoC thinking emphasizes simultaneous, relational, and perspective-sensitive causation. While models such as system dynamics and causal loop diagrams (CLDs) are powerful tools for simulating system behavior over time, they often assume that causal connections are feedback loops and that nodes accumulate stocks (i.e., bathtubs)—assumptions that do not hold for all causal structures. WoC relaxes these assumptions, offering a cognitively accessible and flexible approach that enables individuals and groups to reason through causal complexity in real-world contexts. Connect-the-Dots (CtD) is a complementary framework that builds on WoC by offering a metacognitive strategy for assembling coordinated interventions that engage multiple causes simultaneously. While Rittel and Weber emphasized stakeholder disagreement and solution consequences, we extend their framing to include the cognitive misalignment between mental models and systemic reality, a perspective aligned with VUCA challenges and LAMO thinking. In short, WoC helps us see the full causal landscape of a problem, while CtD helps us act within that landscape to design systemic, multi-factor solutions. WoC is understanding the system, while CtD is the mirror image of action steps that must be taken to address this understanding.

To ground this paper in empirical evidence, we draw upon three distinct but complementary studies that collectively build a robust case for the adoption of Webs of Causality (WoC) thinking in addressing wicked problems.

2. A Systematic Literature Review: Addressing Causal Complexity Across Disciplines

To contextualize the contribution of Webs of Causality (WoC) and Connect-the-Dots (CtD), we conducted a systematic literature review (SLR) of 476 peer-reviewed articles across disciplines including public policy, education, healthcare, and systems science. The review focused on 10 key search terms relevant to causal complexity, including causal complexity, wicked problems, multi-causal analysis, networked causality, and root cause analysis (RCA). Each term’s top 50 cited articles were evaluated using AI-supported human intelligence for meta-synthesis and analyzed through topic modeling (LDA), cluster analysis of methodologies, and citation network mapping.

Key Findings

The review yielded five cross-cutting themes that underscore the limitations of traditional approaches and illuminate the unmet need for operationalized causal thinking frameworks:

• Unseen Interactions: Most failures in complex systems arise not from what is visible, but from what goes unseen—feedbacks, interactions, and ripple effects. Interventions based on linear models often backfire due to these overlooked dynamics.
• Partial Complexity: Many systems are analyzed through fragmented or siloed lenses. This leads to partial diagnoses, ineffective solutions, and “problem displacement” rather than resolution.
• Root Cause Reductionism: While RCA dominates sectors like healthcare, the literature overwhelmingly critiques it for producing shallow fixes, reinforcing biases, and generating a false sense of certainty. Across domains, there is a clear mismatch between complex realities and the oversimplified causal assumptions of RCA.
• Aspiration Without Protocol: Even when systems thinking is invoked, few articles provide actionable protocols for engaging with causal complexity. Most calls for systems change remain aspirational rather than procedural.
• Ideological Capture: Cause selection is frequently influenced by political identity or institutional bias. This leads to polarization, with different stakeholders championing different “primary” causes—often aligned with their worldview. The Perspective Circle (PCircle) move, a core part of the CtD method, directly addresses this by revealing stakeholder frames and preventing cause-level politicization.

Theoretical Contribution of WoC/CtD

This SLR substantiates the central claim that while the existence of causal complexity is widely recognized (87% of articles), fewer than 10% offer structured tools for understanding or acting on it. This evidentiary gap validates the contribution of WoC and CtD as frameworks that move beyond conceptual acknowledgment toward practical application. Unlike system dynamics or causal loop diagrams, which require advanced modeling skills and often assume feedback-based structures, WoC permits representation of non-feedback, non-stock-node systems, making it more cognitively and contextually accessible.

Moreover, the CtD protocol—including moves like Is/IsNot List, Zoom In, Zoom Out, Part Party!, RDS Barbell, and P-Circle—provides a stepwise cognitive infrastructure for applying systemic reasoning across stakeholder groups that goes far beyond the almost entirely relational stance of system dynamics and causal loop diagramming. In doing so, it transitions systemic insight into coordinated action and aligns with reality much more robustly.

Unlike traditional problem structuring methods such as Soft OR, Rich Pictures, SODA, or causal loop diagramming in System Dynamics, WoC/CtD offers a universal approach (rather than specialized to one domain or type of problem set), functions at the level of the individual and the collective, is empirically validated, and explicitly links diagnosis (WoC) to intervention (WoAC) through repeatable cognitive processes (Moves). Rather than competing with these methods, WoC/CtD complements them by filling the gap in cognitively accessible, widely teachable methods for everyday and institutional use.

3. Contribution of This Paper

This paper introduces the concept of Webs of Causality (WoC) as a cognitively grounded, empirically supported framework for understanding and intervening in wicked problems. While the literature on systems thinking is rich with calls to “think systemically,” few studies provide the following:

• Clear contrast between one-cause, root-cause, and systems-based models of causality;
• Empirical validation for metacognitive tools that improve systems thinking;
• A practical, teachable protocol for applying causal complexity in policy contexts.

Drawing on four complementary studies this paper demonstrates the following:

• One-cause and root-cause thinking dominate public and policy discourse;
• Even minimal exposure to structured metacognitive prompts can significantly improve systems reasoning;
• The 6 Moves framework offers a replicable, scalable method for analyzing complex causal systems and designing integrated solutions.

What is novel about this paper is not the standalone introduction of WoC—which has appeared in previous conceptual work—but the integration of WoC, WoAC, and CtD into a single, empirically grounded framework. Additionally, this paper uniquely presents the 6 Moves as a teachable, repeatable cognitive protocol for executing WoC analysis and transitioning from causal analysis to simultaneous systems intervention. This integration, combined with new empirical data and synthesis across domains, constitutes the primary contribution of this work.

4. Methods

This paper draws upon four independent but thematically aligned studies that collectively form the empirical and conceptual basis for the Webs of Causality (WoC) framework and its associated analytical method, the 6 Moves. Each study contributes evidence—experimental, survey-based, or case-based—highlighting the limitations of traditional causal frameworks and the need for cognitively grounded, systems-based approaches to wicked problem solving.

• Study 1: The Fish Tank Experiments [3]

This collection of four studies tested whether brief exposure to prompts based on the DSRP theory (Distinctions, Systems, Relationships, and Perspectives) would increase cognitive complexity. Participants (N = 1400) from a nationally representative U.S. sample were asked to describe an image of a fish tank. After providing an initial description, participants received a short cognitive prompt aligned with one of the four DSRP structures, and then gave a second description. The researchers conducted four separate experiments (one for each DSRP structure) and analyzed the before/after responses using multiple metrics of conceptual and linguistic complexity. Results showed statistically significant increases in systems thinking and conceptual richness following DSRP-based interventions, suggesting that even minimal exposure to metacognitive structures can meaningfully improve complex reasoning.

• Study 2: Framework Effectiveness and Prevalence in Public Policy [4]

This study used a mixed-methods design consisting of a systematic literature review (SLR) and a national survey experiment to examine the effectiveness and usage of frameworks in policy design. The SLR reviewed 59 peer-reviewed publications across multiple disciplines to identify the most common frameworks used in public policy contexts. The analysis revealed that most frameworks—such as SWOT, logic models, and cost–benefit analysis—are applied descriptively and lack empirical validation. Only 13 of the 59 studies reported statistically significant findings, highlighting a major gap in evaluation rigor. In parallel, the study conducted a survey experiment with (N= 286) participants from various sectors: policy professionals, educators, and public-sector workers across domains. Results showed that despite facing complex and interdependent challenges, respondents primarily relied on non-systemic tools, with SWOT and logic models being the most frequently used. The study concludes that the mismatch between tool complexity and problem complexity presents a significant barrier to effective policy design, and calls for the adoption of agent-based, systems-oriented frameworks aligned with the nature of wicked problems.

• Study 3: Wicked Solutions for Wicked Problems [5]

This study combined a systematic literature review with an original survey experiment to introduce and evaluate the Webs of Causality (WoC) model as a systems-based alternative to one-cause and root-cause approaches. The literature review examined 59 peer-reviewed publications on policy frameworks, of which only 13 reported statistically significant findings, highlighting a general lack of empirical validation across existing tools. In the empirical portion of the study, a national survey was conducted with 413 participants representing a demographically balanced sample of U.S. residents. Participants were shown an evidence-based WoC map of causes contributing to mass killings—derived from the Violence Project dataset—and asked to evaluate the effectiveness of various policy solutions. Despite the systemic presentation, most respondents selected a single dominant cause, typically aligned with their political ideology. Only 29% of participants demonstrated any systemic reasoning, and no respondent rated all policy interventions as “extremely effective”—despite expert consensus that all are necessary for meaningful change. These findings reinforce the idea that access to causal data is insufficient; cognitive tools and structured metacognitive training are necessary to counter one-cause bias and foster integrative problem solving.

• Study 4: New Hope for Policy Schools [6]

This study employed a mixed-methods approach, combining a systematic literature review (SLR) with a quasi-experimental survey intervention to evaluate the effectiveness of systems thinking frameworks in policy education. The SLR component reviewed scholarly and curricular literature on systems thinking in public policy education, revealing a continued overreliance on conceptual models and a lack of empirically validated approaches. The review supported the need for cognitive tools that could help students better navigate the complexity of wicked problems.

To test one such tool, the authors implemented a short video-based intervention introducing participants to one of five cognitive “moves” derived from DSRP theory. Each participant (N = 988) responded to a hypothetical policy scenario before and after viewing a brief educational video illustrating one of the DSRP-based moves, yielding 1976 paired observations. The results showed significant improvements in participants’ systems thinking capabilities, with 200–500% increases in move usage post-intervention. These findings provide the first population-level empirical evidence that brief training in the 6 Moves can improve causal modeling and problem framing, offering direct implications for curriculum design in policy education.

5. Results

• Study 1: The Fish Tank Experiments [3]

• Brief DSRP prompts led to statistically significant increases in cognitive complexity across four experiments (Distinctions, Systems, Relationships, Perspectives).
• For example, Distinction prompts increased word use by 43% and conceptual complexity by 41% (IRR = 1.43 and 1.41, p < 0.001).

• Study 2: Framework Effectiveness in Policy [4]

• Survey (N = 286) showed that 46% of policy professionals use no framework.
• Of those who do, most use SWOT and CBA—tools with little empirical support and poor fit for complexity.
• A 59-study literature review found that only 13 reported statistically significant results.

• Study 3: Wicked Solutions Survey [5]

• Survey of 413 U.S. adults asked participants to rate solutions to a mass shooting WoC.
• In total, 71% selected a single dominant cause, aligned with ideology.
• No participant rated all causes as “extremely effective,” despite expert consensus.

• Study 4: New Hope for Policy Schools [6]

• In total, 988 participants tested 6 Moves via short video prompts.
• Participants showed 200–500% increases in causal reasoning and problem-solving across the moves.
• Move usage improved significantly post-intervention (1976 paired responses).

6. Contrasting Mental Models of Causality

When thinking about what causes something to occur, there are several mindsets used. Figure 1 illustrates the cognitive evolution from simplistic to complex models of causality. One-cause thinking, while intuitively appealing, assumes that a single factor is responsible for an outcome—often aligning with ideological preferences and leading to oversimplified solutions. Root-cause thinking takes a step further by identifying a single foundational factor from which all others supposedly emerge, yet still neglects interaction effects and systemic complexity and is overly linear for problems that are non-linear in nature. In contrast, Webs of Causality (WoC) thinking embraces the dynamic interplay of multiple, interconnected causes.

Table 1. Comparison of thinking models.

Causality Model	Core Logic	Strengths	Limitations	Example
One-Cause Thinking	A single factor explains the problem	Simplicity; persuasive storytelling	Oversimplifies; ignores interacting variables	“Mass shootings are caused by mental illness.”
Root-Cause Thinking	A linear path leads to a fundamental source	Deeper than surface symptoms	Assumes hierarchy; neglects feedback and co-causality	“The real cause is family breakdown.”
Webs of Causality (WoC)	Multiple interdependent causes interact to co-create outcome	Matches real-world complexity; supports multi-factor intervention	Harder to teach and act on; cognitively demanding	“Mass shootings arise from a complex mix of factors: access to guns, mental health, school security, social fragmentation.”
“Where one-cause thinking seeks a single villain, and root-cause thinking hunts for the crime boss, WoC tracks all the co-conspirators.”

expands on this progression by comparing the assumptions, solution styles, policy implications, and common cognitive errors associated with each model. Together, the figure and table reveal the limitations of traditional causal models and highlight the need for a paradigm shift toward WoC thinking in addressing wicked problems.

Understanding these three models is not a matter of preference—it determines the success or failure of policy interventions.

One-cause thinking operates on the assumption that every problem can be traced back to a single dominant cause. This logic is appealing because it simplifies complexity into a manageable narrative, offering a clear culprit and a seemingly straightforward solution. It reflects the kind of linear, cause-effect logic often seen in headlines or political soundbites—e.g., “Mass shootings are caused by lack of gun control.” However, this model fails catastrophically in complex systems because it ignores the interacting variables, contextual dynamics, and feedback loops that generate the problem in the first place. Worse, one-cause thinking is highly susceptible to political and ideological hijacking, as individuals or groups attach their values to a preferred explanation, dismissing alternative or complementary causes. As a result, it often leads to ineffective policy responses and misdirected resources.

Root-cause thinking goes a step further than one-cause thinking by attempting to trace surface-level problems back to their most fundamental origin. It assumes a hierarchy of causation—where one deeper issue is the “true” source from which all others emerge. This logic is frequently used in business, engineering, and organizational development through methods like “5 Whys” or fishbone diagrams. While seemingly more sophisticated than one-cause thinking, root-cause analysis still falls short in the context of wicked problems because it prioritizes linear depth over lateral interconnection. Complex problems, like trees, are rarely reducible to a single root; they emerge from multiple, interacting, and co-evolving factors as shown in Figure 2. Root-cause thinking can obscure important surface-level dynamics, reinforce confirmation bias, and lead to paralysis by analysis—endlessly searching for a mythical “core” issue instead of engaging with the system as a whole.

Webs of Causality (WoC) thinking embraces the reality that most meaningful problems—especially wicked ones—arise from a network of interacting, co-dependent causes. Instead of seeking a single cause or root, WoC thinking maps the system of causes and their relationships, acknowledging that each one may be necessary but not sufficient on its own. This model reflects how real-world problems function: dynamically, contextually, and often non-linearly. Its logic aligns with systems science and complex adaptive systems theory, recognizing that solutions must also be simultaneous, systemic, and iterative. While WoC thinking requires more cognitive effort and coordination, its strength lies in its accuracy and efficacy. It prevents simplistic fixes and politicized blame games, and instead offers a path toward multi-factor, collaborative interventions that match the complexity of the problem.

Where one-cause thinking seeks a single villain, and root-cause thinking hunts for the crime boss behind it all, Webs of Causality tracks all the co-conspirators working together. This shift is not semantic—it redefines how we must think, plan, and intervene.

7. Mass Shooting WoC and Political Polarization

One study [5] investigated how people respond to a well-documented WoC surrounding mass shootings. Despite being presented with a list of interrelated causes (i.e., Firearm Access, Mental Health Gaps, Lack of Mentorship, Violent Media Exposure, Unaddressed Trauma, Crisis Response Failures, Low Community Reporting, Mental Health Stigma, Safety Team Deficits, Shooter Glorification, Social Isolation, and Public Unawareness) empirically derived from the largest database of mass shootings (see Figure 3), most participants selected one cause as dominant, often aligning with their political affiliation. This tendency to isolate and politicize a single node in the WoC shows how polarization undermines collective understanding and action.

The study demonstrates a key insight: even when the public is made aware of a complex web of contributing factors, they revert to one-cause thinking that conforms to their ideological bias. This warrants concern because it also means that the general public, as voters, will lean toward one-cause or root-cause political messages and platforms that do not connect the dots.

Although not named explicitly in the study, the WoAC logic underpins the solution set participants were asked to rate. Each proposed solution was an anti-cause—designed to reduce the influence of a known node in the WoC. The study’s finding—that no respondent rated all solutions as effective—highlights the importance of explicitly teaching the WoAC model and the cognitive shift it entails.

8. Politicization of Causes and the Role of Perspective

Figure 4 visualizes the dynamics involved with a WoC structure when combined with a perspective circle (PCircle) resulting in a failure of collective analysis, in which individuals or groups fixate on a single cause within a larger causal web based on their perspective or ideological alignment. This failure of collective analysis does not refer to a group deliberating together per se, but to the societal-level outcome that emerges when individuals, each biased by politicized or partial perspectives, converge on fragmented causal beliefs. This leads to collective behavior—such as elections, purchasing, or public support—that fails to align with the systemic realities of the problem. This diagram demonstrates how two distinct ideological camps can each elevate different nodes in the same Web of Causality—such as focusing exclusively on gun access or mental health in the context of mass shootings—while ignoring the broader systemic interplay. The result is a fractured understanding of the problem, with each group advocating for isolated solutions and often dismissing or discrediting alternative views. This polarization not only undermines constructive dialog but also impedes the implementation of integrated, systemic solutions.

This diagram uses the Perspective Circle (P-Circle) move to illustrate how different ideological groups engage with a shared Web of Causality (WoC) surrounding mass shootings. Although the central causal map includes multiple empirically supported contributors—such as access to firearms, mental health gaps, school security, social isolation, and media violence—each stakeholder group tends to focus on just one cause that aligns with their worldview. Gun control advocates emphasize firearms access; others point to mental health gaps and shooter glorification in the news. This selective emphasis fragments the system and impedes coordinated, multi-factor solutions.

Table 2. Examples of ideological capture of causal nodes.

Wicked Problem	WoC Node	Political Ideology	Polarizing Narrative
Climate Change	Fossil Fuels	Progressive/Green	‘It’s all oil companies’
		Conservative/ Conspiratorial	‘It’s a hoax.’
School Shootings	Gun Access	Conservative	‘It’s not guns, it’s mental health’
		Liberal	‘It’s not mental health, it’s the guns’

provides real-world examples of how specific nodes within a Web of Causality become ideologically captured, turning multifactorial problems into battlegrounds of political identity. In the case of climate change, for example, progressive narratives may disproportionately emphasize fossil fuels while minimizing other systemic contributors such as consumption patterns or global inequality. Conversely, conservative narratives around school shootings often highlight mental health issues while downplaying or rejecting the role of gun access. This selective attention distorts the overall problem space and creates barriers to collaborative, systems-based policymaking. By mapping these ideological captures, the table underscores the urgent need for depolarized, perspective-inclusive approaches to wicked problems.

Webs of Causality (WoC) account for the reality that stakeholders often hold divergent views on which causes matter most, and why. These divergences can become entrenched, especially when individual nodes in the web become aligned with ideological, political, or cultural identities. Using the PCircle (Perspective Circle) move, the framework surfaces these differences as perspectival rather than factual, allowing for a depolarized analysis of the full causal landscape. Instead of competing over which node is “the” cause, stakeholders are guided to recognize that different perspectives may highlight different but valid contributors to the system. This approach reduces the tendency to politicize individual causes and reframes conflict as a difference in focus rather than a disagreement on fact—enabling a more productive, integrative problem-solving process.

9. From Problem to Solution: Webs of Anti-Causes (WoAC)

While a Web of Causality maps the multiple interacting causes of a wicked problem, its inverse—the Web of Anti-Causes (WoAC)—offers a solution space. Anti-causes are interventions that reduce the likelihood, severity, or recurrence of causal contributors. For example, if “Access to Firearms” increases risk, then “Safe Storage Laws” may reduce it. If “Social Isolation” contributes to radicalization, then “Community Engagement Programs” can disrupt that pathway. The WoAC is not a list of isolated fixes; it is a network of coordinated interventions designed to work together. This model explains why one-at-a-time policies often appear to fail: not because they are ineffective, but because they are deployed in isolation, without reinforcement from the broader causal system they aim to address.

In the Web of Causality (WoC), we map the multiple interacting causes that contribute to a wicked problem. But embedded in that web—like a mirror image—is a powerful structure we call the Web of Anti-Causes (WoAC).

An anti-cause is a factor that has the opposite directional effect of a cause. Where a cause increases the likelihood, severity, or persistence of a problem, an anti-cause decreases it. Where a cause increases the problem, an anti-cause decreases the problem. For example: if a cause of cancer is increased smoking, then an anti-cause is reduced smoking. If social isolation increases depression, then social support networks act as anti-causes.

When we map out all the anti-causes for a given WoC, we obtain the Web of Anti-Causes (WoAC)—a structurally parallel map that highlights the most important points for systems-level change.

By flipping a web of causes into a web of anti-causes (Figure 5), we obtain a critical insight: solutions to problems are a Web of Anti-Causes.

Solutions are not merely a list of good ideas, they are a simultaneous, systemic activation of multiple anti-causes, designed to reverse the problem state. A one-at-a-time solution may fail because it relies on a single anti-cause, which is often insufficient against the full complexity of the WoC. An effective solution uses the entire WoAC—multiple anti-causes in concert—to make real systems change. Thus CtD is the process of connecting the dots on the WoC and the WoAC. The question then becomes how to effectively build the WoC so that you can flip it into a WoAC solution.

To support the shift from fragmented to systemic analysis, we introduce a validated, teachable cognitive method: the 6 Moves.

10. The 6 Moves: A Cognitive Protocol for Mapping Webs of Causality

To operationalize Webs of Causality (WoC) thinking, we introduce a validated cognitive framework called the 6 Moves. Derived from DSRP theory (Distinctions, Systems, Relationships, and Perspectives), these Moves provide a structured yet flexible method for surfacing causal complexity, mapping systems, and designing multi-factor interventions. Where traditional policy frameworks like SWOT or logic models offer linear or siloed views, the 6 Moves (

Table 3. The 6 most effective cognitive Moves.

Move	Purpose
Is/Is Not	Defines the problem by clarifying what it is and what it is not—exposing boundary assumptions and surfacing overlooked variables.
Zoom In	Scales the problem contextually, helping users understand micro-level details
Zoom Out	Scales the problem contextually, helping users macro-level dynamics simultaneously.
Part Party!	Identifies parts, agents, and nested subsystems involved in the problem—making visible the components often left implicit.
RDS Barbell	Surfaces relationships, directionality, and feedback loops—revealing how causes interact and co-determine outcomes.
Perspective Circle	Makes visible the perspectives that stakeholders hold, and how these perspectives shape what causes they see, prefer, or ignore.
Mapping	Integrates all the above into a cohesive visual model that reveals the full Web of Causality, supporting collective understanding and intervention design.

) enable practitioners to think systemically—identifying boundaries, components, interdependencies, feedback loops, and ideological perspectives that influence both the problem and its proposed solutions.

The Moves are not merely conceptual—they are designed to be practiced, repeated, taught, and evaluated. They can be used individually but are more appropriately used together to identify all of the variables in the system. In New Hope for Policy Schools [6] (Study 4), brief video-based interventions introducing participants to one of the 6 Moves led to statistically significant increases in causal reasoning, systems awareness, and problem-framing quality. Over 1900 paired observations showed 200–500% increases in accurate usage of systems concepts after just one exposure. This provides strong empirical evidence that the Moves can shift how individuals conceptualize and address wicked problems. Each Move targets a specific dimension of systems thinking. Together, the 6 Moves function as a repeatable protocol for cognitively navigating complex problems. They are designed to be simple enough to teach, yet powerful enough to surface the deep structure of wicked issues. In this paper, we propose that the 6 Moves represent a viable step toward standardizing systems thinking as a practical cognitive methodology—not just a theoretical aspiration.

11. The Empirical Basis for DSRP-483 and 6 Moves

Empirical research into the cognitive impact of DSRP patterns and their associated “Moves” demonstrates a significant increase in cognitive complexity, problem solving, and higher-order thinking—even following minimal exposure. In a large-scale experimental study [3] involving over 1400 participants, the Fish Tank Experiments revealed that a brief (<1 min) metacognitive intervention on each DSRP pattern led to statistically significant gains in the complexity and richness of participants’ thinking across all four cognitive structures: Distinctions, Systems, Relationships, and Perspectives [7,8,9,10].

Specifically, the Distinction pattern (identity vs. other) produced a 43% increase in word use and a 41% increase in the complexity of those words, as measured by character length—a robust proxy for conceptual complexity. This effect size (IRR = 1.43 and 1.41, p < 0.001) suggests that simply prompting individuals to identify and differentiate elements in a system immediately improves their descriptive and analytical precision.

The Systems pattern (part–whole structuring) led to a 13.2% increase in total word use (p = 0.13), a 41.6% increase in unique word usage (p < 0.001), and a remarkable 49.2% increase in the complexity of those unique words (p = 0.13). While the change in total word use was not statistically significant, the increase in unique word usage—reflecting broader conceptual differentiation—was statistically significant, indicating enhanced part-whole reasoning.

Similarly, the Relationships pattern (action–reaction) increased word use by 15.5% (p < 0.001) and enriched the relational language and conceptual depth participants employed. Subjects were more likely to describe the interactions among elements, rather than listing them as isolated entities.

The Perspectives pattern (point–view) intervention resulted in a 16.5% increase in word use (p = 0.064), a statistically significant increase in character complexity (p = 0.013), and a significant increase in the number of concepts described (p = 0.002). These findings demonstrate that perspective-taking, even when briefly introduced, can enhance narrative complexity and foster more inclusive or multidimensional reasoning.

In parallel, the Moves study ([6], in review) tested these same cognitive patterns operationalized as “thinking moves” and assessed their practical efficacy independently. The results showed dramatic improvements across domains of problem solving, higher-order thinking, and emotional intelligence (

Table 4. Effects of minimal interventions of DSRP and cognitive moves based on DSRP.

Pattern and Elements	DSRP Findings After <1 m Intervention	Move(s)	Moves Findings After <1 m Intervention
Distinction (D) = identity (i) ⇔ other (o) D = i ⇔ o	+43% increase in word use, +41% increase in character complexity (IRR = 1.43 and 1.41, p < 0.001)	Is/Is Not List Move	5.51× or 551% increase in problem solving, higher order thinking and emotional intelligence (p < 0.01)
Systems (S) = part (p) ⇔ whole (w) S = p ⇔ w	+13.2% word use, +41.6% unique words, +49.2% increase in character complexity of unique words (p < 0.001 for number of concepts)	Zoom In Move Zoom Out Move	2.66× or 266% increase in problem solving, higher order thinking and emotional intelligence (p < 0.01)
Relationships (R) = action (a) ⇔ reaction (r) R = a ⇔ r	+15.5% in word use (p < 0.001), richer relational language and concept depth	Part Party! Move RDS Barbell Move	2.47× or 247% increase in problem solving, higher order thinking and emotional intelligence (p < 0.01) 5.08× or 508% increase in problem solving, higher order thinking and emotional intelligence (p < 0.01)
Perspectives (P) = view (v) ⇔ point (ṗ) P = v ⇔ ṗ	+16.5% increase in word use, significant increase in varied perspectives and narrative complexity (p = 0.002 for concepts; p = 0.013 for characters)	Perspective Circle Move (P-Circle)	4.47× or 447% increase in problem solving, higher order thinking and emotional intelligence (p < 0.01)

). The Is/Is Not List Move (linked to Distinction) resulted in a 5.51× (or 551%) increase in performance. Zoom In/Zoom Out Moves (related to Systems) showed a 2.66× (266%) improvement. The Part Party! Move and RDS Barbell Move (both reflecting the Relationships pattern) led to 2.47× and 5.08× gains, respectively. Finally, the Perspective Circle Move, which engages the Perspective pattern, produced a 4.47× (447%) increase in problem solving and emotional intelligence scores.

Together, these two lines of evidence—from cognitive complexity measures and applied problem-solving performance—provide a strong empirical foundation for the use of DSRP-based interventions and thinking Moves in increasing systems’ thinking capacities. They also underscore the potency of even short-term interventions, suggesting that long-term or repeated use would likely lead to transformative cognitive and practical gains.

The moves study [6] tested a experimental intervention involving short video trainings on six DSRP-based cognitive moves: Is/Is Not, Zoom In, Zoom Out, Part Party!, RDS Barbell, and P-Circle. Each move targets a core aspect of systems thinking and is designed to be content-agnostic and universally applicable (Figure 6).

12. The Connect-the-Dots Model: From Analysis to Synthesis

Once we understand the web, we must act on it. CtD represents the synthesis phase: designing simultaneous interventions that address multiple nodes in a WoC. This approach avoids the trap of discrediting a valid solution simply because it was tried in isolation (Figure 7). Instead, it combines causal factors in coordinated strategies. Connect-the-Dots (CtD) enables decision-makers to coordinate multiple causal interventions simultaneously, aligning them in strategies that address the interconnected nature of the problem. Rather than treating causes in isolation or sequentially, CtD guides users to design solutions that act on multiple nodes of the Web of Causality at once, thereby maximizing systemic impact.

For example, in the school district case (Section 12), Connect-the-Dots thinking enabled leaders to design a coordinated solution: they implemented home visits (targeting social disengagement), shifted attendance communications (addressing punitive culture), and introduced community mentorship (counteracting lack of role models). These interventions, drawn from the WoAC, were connected intentionally—avoiding the pitfalls of one-at-a-time fixes.

The six moves offer practical tools to design such multi-pronged solutions. For example, using P-Circle helps depolarize perspectives by mapping various stakeholder views; Part Party! and RDS Barbell reveal the interconnections and then zoom into what is occurring inside the relationships; Zoom In and Zoom Out balance the micro and macro; and Is/Is Not clarifies boundaries.

13. Implementation: Practicing the 6 Moves

The 6 Moves are not theoretical abstractions—they are actively used in educational, organizational, and policy settings. Because they are grounded in how people actually think (via DSRP), they are teachable, flexible, and scalable. We have used the Moves with students, leaders, analysts, and practitioners to unpack real-world problems ranging from youth violence and PTSD to climate adaptation and school reform. In practice, users often cycle through the Moves non-linearly, gradually refining their WoC map and translating it into a Web of Anti-Causes. This recursive process builds shared understanding and supports multi-factor, systems-informed action.

Case Vignette: Using the 6 Moves in a School District

In a mid-sized public school district facing a sharp increase in chronic absenteeism and student disengagement, district leaders used the 6 Moves to unpack the problem systemically. The initial framing—“kids just don’t care about school anymore”—reflected one-cause thinking. Using Is/Is Not, the team clarified what absenteeism was and was not, surfacing issues like food insecurity and transportation barriers. Zoom In/Out revealed how day-to-day decisions (e.g., punitive attendance policies) intersected with broader community factors like housing instability. Part Party! mapped the relationships between these multiple causes. RDS Barbell zoomed into these relationships to reveal reinforcing loops between disengagement and academic performance, punitive policies and demotivation, lack of engagement and agency in curriculum. P-Circle exposed how different stakeholders saw completely different causes—and blamed each other. The final Mapping session produced a Web of Causality that led directly to a solution plan (WoAC) including home visit outreach, community mentorship, and changes to how attendance was communicated. The Moves helped shift the conversation from blame to systems change and a solution that worked.

14. Methodological Comparison: Webs of Causality vs. System Dynamics

• Foundational Logic and Assumptions

System Dynamics (SD) is a modeling methodology grounded in feedback theory, often using Causal Loop Diagrams (CLDs) and Stock and Flow Diagrams (SFDs) to simulate system behavior over time. Its strengths lie in representing dynamic feedback loops, delays, and accumulations (stocks) to predict long-term behavior of systems. However, SD models generally assume that all causality occurs via feedback and that systems behave analogously to bathtubs (i.e., stock accumulation), which may not apply to all causal networks (

Table 5. Summary comparison table.

Dimension	System Dynamics (SD)	Webs of Causality (WoC) + CtD
Core Assumption	Feedback loops, stock/flow structures	Structural agnosticism, multi-form causality
Training Requirement	High (technical modeling, software-based)	Low (cognitive protocol, no software required)
Stakeholder Engagement	Low (expert-driven modeling)	High (collective, participatory mapping)
Integration with Action	Weak (diagnostic focus)	Strong (WoC → WoAC via CtD protocol)
Empirical Support	Theoretical and simulation-focused	Strong empirical validation
Cognitive Accessibility	Low	High
Theoretical/Cognitive Depth	Moderate (feedback logic, simulation theory); not ontologically or epistemologically grounded	High (epistemologically and ontologically grounded, structurally universal via DSRP)

).

Webs of Causality, by contrast, adopt a structurally agnostic approach to causality. WoC maps may include feedback loops but do not assume them; they are capable of representing linear, non-linear, hierarchical, acausal, or co-causal interactions without relying on a simulation framework. This flexibility allows for modeling complex social, behavioral, political, or ecological systems that may not conform to accumulations or feedback-dominant logic. In keeping with the principle of parsimony, DSRP-based WoC can represent all forms of structure that System Dynamics can—and many that it cannot—without the epistemological overhead of assuming feedback as the dominant form. The reverse, however, is not true.

• Cognitive Accessibility and Use Context

SD typically requires formal training in systems modeling, mathematical understanding of feedback theory, and proficiency with modeling software like Vensim or Stella. This limits its use to highly trained experts and often removes real-time, collaborative engagement from policymaking processes.

By contrast, the WoC/CtD framework is designed for cognitive accessibility and collective reasoning. It requires no technical tools and can be deployed through structured conversations, whiteboards, or simple digital interfaces. The six Moves—Is/Is Not, Zoom In, Zoom Out, Part Party!, RDS Barbell, and Perspective Circle—provide a repeatable cognitive protocol that enables individuals and groups to surface, navigate, and act on causal complexity collaboratively.

• Diagnosis and Intervention Integration

While SD provides a degree of diagnosis and simulation, it often stops short of offering a practical protocol for designing and coordinating interventions. In contrast, the WoC approach explicitly links diagnosis (mapping the causal web) with intervention (Web of Anti-Causality or WoAC), which is further operationalized through CtD. This integration ensures that understanding systemic causation leads directly to multivariate, simultaneous solutions—a core requirement for addressing wicked problems.

Empirical Validation and Psychological Realism

SD has a rich tradition of theoretical modeling and policy simulation, but lacks widespread empirical research on its cognitive effects or educational interventions at the population level. In contrast, the WoC/CtD model is grounded in cognitive science and metacognition:

• The Fish Tank experiments showed that short interventions based on DSRP significantly increased participants’ cognitive complexity by 200–500%.
• The New Hope for Policy Schools study confirmed that brief exposure to one of the Six Moves significantly enhanced systems thinking and causal reasoning across a nationally representative sample.
• The Wicked Solutions study demonstrated the persistence of one-cause bias and ideological distortion—even when presented with a known WoC—underscoring the need for metacognitive tools like CtD to overcome them.

These findings provide robust empirical support for the WoC/CtD model’s educational and behavioral effects in both public and policy populations.

While SD is often seen as more “technical” due to its use of software and simulation, this should not be mistaken for greater theoretical depth. DSRP, the foundation of WoC/CtD, operates at a more fundamental cognitive, epistemological, and ontological level. It captures the underlying structures that make modeling possible, enabling both technical application and deep cognitive insight. In this sense, WoC/CtD is not only more flexible and accessible, but also more advanced.

15. Discussion

From the boardroom to the clinic, and from national policy to personal well-being, problem-solving frameworks are central to how we navigate complexity. Yet most conventional frameworks in use today—such as SWOT or cost–benefit analysis (CBA)—are empirically unvalidated and cognitively misaligned with the reality of complex adaptive systems (CAS). As a systematic review of policy frameworks confirms, these tools persist due to institutional inertia, not evidence of effectiveness.

In contrast, the Connect the Dots (CtD) framework—grounded in DSRP Theory (Distinctions, Systems, Relationships, and Perspectives)—has been empirically shown to increase cognitive complexity and systems thinking. The Fish Tank Experiments demonstrate that even a one-minute intervention on DSRP patterns yields statistically significant gains in systemic cognition. CtD is not merely another framework—it is a generalizable, metacognitive infrastructure for understanding webs of causality (WoC) and crafting effective interventions.

The application of WoC and CtD thinking extends across a continuum of complexity and across domains, including the following:

• Public Policy and Mass Shootings: Research (2021) [11] shows that despite widespread awareness of the multifactorial causes of mass shootings [11], public opinion and political discourse consistently reduce the issue to a single politicized cause (e.g., guns or mental health), ignoring the full web of causality. This leads to ineffective, polarized policy responses. CtD enables policymakers to embrace the multi-causal reality and design simultaneous interventions.
• Medicine and Holistic Health: Physician and metabolic health expert Dr. Casey Means [12] argues that chronic diseases are not isolated events but symptoms of a dysregulated system. Like CtD, her approach to medicine emphasizes seeing the whole system, recognizing upstream drivers, and intervening on multiple fronts simultaneously—metabolic, environmental, lifestyle, and emotional.
• Longevity and the 12 Gets: Dr. Peter Attia [13] highlights that optimizing for longevity is not a single variable challenge but a multi-system, multi-causal puzzle. His work aligns with the Cabrera’s [14] emphasis on the 12 Gets (e.g., Get Strong, Get Stable, Get Nourished, Get Sleep, etc.), which require parallel development, not sequential or one-cause interventions. This model exemplifies simultaneous WoC-based solutions in practice.
• Nutritional Health: It is often the case that the next new thing is one thing–fad diets that encourage you to focus on eating or not eating one thing or taking one silver bullet supplement. This, despite the fact that nutrition is a web of causality. CtD helps us move beyond the fads.
• Business Strategy and Organizational Design: Businesses often suffer from single-metric optimization (e.g., profit, KPIs), leading to downstream dysfunction. CtD reframes strategy as a dynamic system of interdependent parts—culture, operations, stakeholders, external forces—helping leaders balance tradeoffs and adapt across multiple feedback loops.
• Therapy and PTSD Recovery: Mental health protocols that focus only on trauma narratives without stabilizing the nervous system or social environment are often ineffective. CtD-informed PTSD recovery protocols use WoC logic to address the biological, relational, behavioral, and cognitive anti-causes in tandem—shifting from one-cause trauma theory to systemic healing maps.
• Cybersecurity and Threat Analysis: Attack vectors are not linear or isolated. A systems approach enabled by CtD helps analysts map threat surfaces, understand attacker behavior, and intervene at multiple weak points simultaneously—aligning defense strategies with the ecology of risk.
• Engineering and Product Design: CtD supports resilient design by making visible the interdependencies between parts, users, environments, and unintended consequences. It enhances failure analysis and supports agile, iterative cycles aligned with real-world feedback.
• Education: Learning is shaped by more than teaching—it emerges from the interaction of students, environments, and systems. CtD helps scaffold interconnected concepts and cultivate systems literacy across these layers, preparing learners for a complex, VUCA world.
• Technology and Systems Design: As technology systems scale in complexity (e.g., AI ethics, data governance), CtD supports better anticipation of emergent behavior and more responsible systems architecture.
• Everyday Problem Solving: From relationships to career choices, CtD helps individuals avoid overgeneralization and reductionism, encouraging a deeper understanding of underlying patterns and multiple interacting causes.

What unites all these domains is a shared challenge: the mismatch between the complexity of the problem and the simplicity of the tool used to solve it. Traditional frameworks impose linear logic on non-linear problems, favor root causes when reality presents webs, and encourage isolated interventions instead of simultaneous strategies.

This mismatch is not just technical—it is cognitive and cultural. Our brains favor simplicity, ideological alignment, and confirmation bias. Public narratives reinforce this with headlines that identify “the” cause, “the” solution. Over time, individuals align their identities and politics with specific causes in a WoC, leading to polarization, paralysis, and misdiagnosis.

The six CtD Moves—Is-Is Not, Zoom In-Zoom Out, Part Party!, RDS Barbell, and Perspective Circle—combat these biases directly. They train users to do the following:

• See boundaries and exclusions (Is-Is Not);
• Adjust granularity (Zoom In);
• Consider the wider context (Zoom Out);
• Recognize webs of causality in systems (Part Party!);
• Adjust granularity in relationships to see dynamics and feedback (RDS Barbell);
• Map diverse stakeholder perspectives (P-Circle);
• Together, they transform how people see, think, and act.

16. Limitations and Future Directions

While this paper presents a novel, empirically supported framework for mapping and intervening in Webs of Causality, several limitations warrant acknowledgment. First, while the studies span experimental, survey, and review methods, more longitudinal research is needed to assess the sustained impact of the 6 Moves over time and in field-based implementations. Second, although the WoC and WoAC models are conceptually transferable, their operationalization across domains may require contextual adaptation, although the power of DSRP remedies this contextual problem. Finally, future research should explore integration with existing policy design protocols and evaluate the Moves in collaborative, high-stakes decision-making environments. We encourage further investigation of how WoC thinking can be scaled in institutions, education, and governance.

17. Conclusions

The demand for systemic solutions has never been higher, and yet our cognitive tools remain outdated. Whether tackling climate change, metabolic health, organizational dysfunction, or personal growth, the key is seeing and acting on webs of causality—not isolated factors. Connect the Dots, grounded in DSRP and supported by empirical research, offers a scientific, scalable, and flexible method to meet this challenge. It is not just a better framework—it is a cognitive upgrade for a complex world.

To solve the most wicked problems of our time, we must stop blaming and start mapping. We must move from asking “What’s the cause?” to “What’s the web?” and from implementing isolated fixes to designing coordinated solutions. Webs of Causality and Connect-the-Dots thinking, powered by the six DSRP-based moves, represent a scientifically grounded, pragmatically powerful, and urgently needed shift in how we understand and act in the world.