J.Konstapel, Leiden, 24-4-2026.
Synthesis of two worlds: The article combines Decision Intelligence (DI) – decision systems as the basis for competitive advantage – with field-based theories of cognition (decisions as dynamic patterns) into a new model of “coherent decision fields.”
Deciding is field coherence: Decision quality is not just good data or models, but the coherence of the entire organizational field: signals, incentives, power, culture, and feedback.
Policy is an attractor: Official rules often diverge in practice due to “shadow policies”; these are best understood as competing attractors within the field, stabilized by power relations and habits.
Governance as coherence regulation: Oversight should not focus merely on compliance, but on maintaining learning capability – where anomalies become diagnostic signals and feedback is actively protected.
Practical implication: Instead of solely designing “better decisions,” aim to strengthen field coherence – for example, through signal diversity, making assumptions visible, and fast feedback loops.
For decades, management theory and cognitive science have pursued parallel questions. Why do some organizations consistently make intelligent decisions under uncertainty, while others fail catastrophically? Decision Intelligence (DI) answers at the organizational level: competitive advantage flows from the quality, speed, and governability of decision systems. Field-based theories of mind answer at the cognitive level: intelligence emerges from coherent, relationally reconstructed fields, not from localized computation. This essay argues that these two frameworks describe the same underlying phenomenon—the transformation of relational input into adaptive action—and that their synthesis into coherent decision fields offers a unified theory with profound implications for governance, strategy, and the limits of algorithmic management.
The Strengths and Limits of Decision Intelligence
DI’s core contribution is the shift from resources to decision systems as the primary unit of competitive analysis. In Moser’s (2026) Decision-Dominant Logic, organizations are not portfolios of assets but flows of decisions. DI operationalizes decision quality across six dimensions—accuracy, speed, cost, robustness, adaptability, fairness—severing quality from outcome bias and making learning possible even from successful outcomes. Its five-level construct hierarchy (instances, policies, routines, architectures, governance) enables systematic improvement, and its integration of AI into accountable governance frameworks is a genuine advance.
Yet DI rests on assumptions that fracture under real-world complexity. The designability assumption presumes that decision policies can be specified top-down, which fails under genuine uncertainty where variables and causal structures are emergent. The modularity assumption treats signal infrastructure, inference, execution, and governance as separable components, ignoring deep entanglements with politics, power, and culture. The rationality assumption frames politics and emotion as obstacles to be engineered away, rather than as constitutive of organizational life. Most critically, DI’s static-policy assumption lacks self-organization: policies are redesigned externally, not reconfigured by the system itself. Where environmental change outpaces deliberate revision, DI’s machinery stalls.
The Field Turn in Cognitive Science
Field-based theories of mind—synthesizing quantum field theory (Vitiello, 2001), enactive cognition (Varela, Thompson & Rosch, 1991), and process philosophy (Whitehead, 1929)—offer a radically different ontology. Cognition is not stored representation retrieved on demand, but dynamic reconstruction from relational patterns. The organizing principle is coherence across temporal, spatial, and functional scales. The primary unit is the relational field, not the individual agent. Intelligence organizes around attractors—stable patterns in a high-dimensional state space—maintained through continuous active processes rather than fixed storage. Learning occurs through attractor reconfiguration; pathology corresponds to decoherence or maladaptive fixation.
These theories are conceptually powerful but operationally underdeveloped. Coherence can be formally defined (Tononi’s integrated information, Friston’s free energy), but translation into organizational practice remains largely undone. The attractor metaphor illuminates but lacks empirical grip. This is precisely where DI’s operational framework can contribute—not as a reduction, but as a practical approximation that preserves essential insights.
Structural Correspondence and Core Integration
When placed in parallel, a direct structural mapping emerges: DI’s signal infrastructure corresponds to sensory coupling; inference mechanisms to dynamic reconstruction; execution pipelines to action enactment; feedback loops to experiential learning; decision governance to meta-regulation; and decision policies to cognitive attractors. This is not superficial analogy. Both describe systems that couple to environments, integrate signals into patterns, translate patterns into action, adjust structure in response to outcomes, and maintain meta-level regulation. The difference is one of abstraction: DI provides the operational grammar, field theory the dynamic ontology.
The central theoretical proposition is this: decision quality is the operational expression of system coherence. Accuracy corresponds to fidelity of dynamic reconstruction. Robustness maps to attractor stability. Adaptability reflects plasticity of attractor topology. Fairness indexes consistency of field coupling across relational inputs. Poor decisions, on this view, are not primarily the result of bad data or poor models—they are the result of incoherence: fragmented signal infrastructure, misaligned incentives, political suppression of feedback, cognitive biases that distort inference. The governance question becomes not merely “are our policies well designed?” but “is the system that produces and maintains those policies coherent?”
Decision policies, in the coherent decision field model, emerge as attractors—stable patterns toward which the system evolves under internal dynamics and environmental pressures. This yields several non-trivial insights. Shadow policies—decision patterns diverging from official rules—are not implementation failures but competing attractors reflecting actual incentives and power relations. Path dependence becomes intelligible: deep attractors resist change not through irrationality but through topological stability; escape requires energy input across the system, not a new policy document. Discontinuous change corresponds to bifurcations: small parameter shifts can produce rapid topological reconfiguration, explaining transformations that incremental optimization cannot.
Governance is reinterpreted as coherence regulation. Ownership establishes local coherence centers; constraints define basins of attraction; monitoring signals decoherence onset; auditing detects chronic drift; escalation routes boundary cases to higher-level coherence resources; accountability closes learning loops. When governance is experienced as external control, systems game metrics and suppress feedback. When governance is understood as self-maintenance of coherence, anomalies become diagnostic signals, overrides are reported honestly, and post-decision reviews produce genuine learning. This maps directly onto DI’s distinction between vicious loops (weak governance → slow learning → degradation) and virtuous loops (strong governance → fast learning → legitimacy).
The Coherent Decision Field Model
The CDF model proposes five core principles. First, decisions are emergent, not only designed: they arise from the full organizational field—incentives, power, cognitive schemas, relational histories, environmental pressures—not solely from intended policies. Second, decision quality is a property of the field, not of individual components: an excellent model in an incoherent field produces poor decisions; a simple policy in a coherent field may produce excellent ones. Third, learning is attractor reconfiguration: genuine organizational learning requires energy input across the system and tolerates transitional instability. Fourth, coherence maintenance is the primary governance function. Fifth, the field is multi-scale: decoherence at one scale propagates to others.
Architecturally, the CDF model comprises four layers. The Relational Coupling Layer (signal infrastructure) concerns undistorted, diverse, timely environmental contact. The Reconstructive Inference Layer (analytics and modeling) includes not only quantitative models but mental models, cultural frames, and embedded assumptions. The Action-Execution Layer translates understanding into action, acknowledging that pipelines are active mediators, not neutral conduits. The Coherence Regulation Layer (governance extended) maintains integration across layers: feedback loops, conflict resolution, and the institutional culture that determines whether coherence is actively maintained or passively decayed.
Pathologies become specific decoherence patterns. Signal fragmentation (data silos) produces reconstructive errors that accumulate into systematic environmental misalignment. Inferential fixation (confirmation bias, model overfit) stabilizes around a single attractor regardless of incoming signals. Execution-inference decoupling (the “pilot graveyard”) disconnects action from updated understanding. Governance decoherence suppresses the feedback required for self-correction—the pathology underlying catastrophic failures like the Zillow case, where governance mechanisms were deliberately disabled.
Implications for Management and Governance
The primary practical shift is from policy optimization to coherence management. Accurate, governable policies remain essential, but the limiting factor in most transformations is not policy quality but field coherence. Practical coherence management involves: actively maintaining signal diversity rather than optimizing for processing efficiency; making reconstructive processes visible, including embedded assumptions; protecting feedback as the primary governance asset; and mapping actual decision patterns as the current attractor landscape.
Governance redesign under CDF follows several principles. Replace compliance with coherence as the objective: compliance asks “are rules followed?”; coherence asks “is the system learning?” Govern at all scales simultaneously: attend equally to informal attractors—shadow policies, cultural norms, leadership behaviors. Design escalation as coherence routing: when a decision falls outside reconstructive competence, re-engage the full relational field, slow down reconstruction, and tolerate temporary decoherence. Embed learning in the governance cycle as an explicit coherence feedback loop, continuously recalibrating the distance between intended and actual decision patterns.
The CDF model deepens DI’s intuition about algorithmic decision-making. Deploying an algorithm does not replace field dynamics with something more tractable—the algorithm becomes a new element, coupling with data pipelines, human reviewers, incentives, and competitive pressures in ways not fully specifiable in advance. The governance challenge is to manage the new attractor landscape that deployment creates. This explains why “governance-last” approaches consistently fail: by the time oversight is added, the algorithm has already reshaped the field’s attractor structure in ways resistant to correction. Automation complacency becomes a slow decoherence of the regulation layer—a gradual loss of self-referential capacity.
Research Directions and Limitations
Empirical research must develop operational indicators of organizational coherence: cross-functional information integration, variance in decision outputs under controlled input variation, feedback velocity. Attractor mapping methods from dynamical systems analysis (phase space reconstruction, recurrence quantification, bifurcation analysis) could be applied to organizational data to identify divergences from intended policies. Comparative case analysis of coherence dynamics under stress could test predictions about decoherence patterns. Computational modeling of multi-scale coupling dynamics could formalize governance principles.
Limitations are significant. The structural correspondence operates at a high level of abstraction; whether it reflects deep homology or family resemblance remains open. The formal apparatus of field theories has not been fully operationalized for organizational contexts. Both DI and field theory tend toward systemic explanations that underweight power, interests, and conflict. And the CDF model is presented in general terms, but attractor landscapes are profoundly shaped by cultural and institutional context—a dimension left unaddressed.
Conclusion
Decision Intelligence and field-based theories of mind describe the same fundamental phenomenon—adaptive transformation of relational input into organized action—at different levels of abstraction. Their synthesis into the coherent decision field model yields a unified theory: organizations are multi-scale coherence-maintaining systems; decision policies are attractors, not merely designed rules; decision quality is the operational expression of system coherence; governance is coherence regulation, not compliance enforcement; organizational learning is attractor reconfiguration, requiring energy and tolerating instability. The limiting factor in most decision-centric transformations is not individual policy quality but the coherence of the organizational field that produces, maintains, and updates them. Recognizing this convergence opens new possibilities for both the science and the practice of decision-making in complex organizations.
Annotated Reference List
The following list includes all works cited in the original PDF, with extended annotations to clarify their relevance to the coherent decision fields synthesis.
Atmanspacher, H. (2011). Quantum approaches to consciousness. Wiley Interdisciplinary Reviews: Cognitive Science, 2(5), 554-563.
Provides a systematic overview of quantum theoretical models of consciousness, including non-locality, complementarity, and state reduction. Essential for grounding the field-theoretic claim that neural dynamics exhibit quantum-like properties irreducible to classical computation. Supports the notion of coherence as a physically meaningful construct.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
The foundational statement of the Resource-Based View (RBV), arguing that sustained advantage derives from resources that are valuable, rare, imperfectly imitable, and non-substitutable. DI’s shift from resources to decision systems is positioned as an explicit departure from RBV. Relevant for understanding what DI intends to supersede.
Baron, J., & Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of Personality and Social Psychology, 54(4), 569-579.
Classic experimental demonstration that people evaluate decisions based on outcomes rather than the quality of the decision process at the time of making. DI’s operationalization of decision quality independent of outcome directly targets this bias. Central to DI’s claim that learning is possible even from successful outcomes.
Bateson, G. (1972). Steps to an Ecology of Mind. University of Chicago Press.
A collection of essays introducing the concept of “mind” as an immanent, relational property of systems (e.g., organism-environment, or organization-ecosystem) rather than a substance inside individuals. Introduces “difference that makes a difference” as the unit of information. Directly anticipates field theories of mind and the relational ontology adopted in the synthesis.
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the ‘good society’. Science and Engineering Ethics, 24(2), 505-528.
Examines governance requirements for AI systems, including accountability, transparency, and the prevention of value lock-in. Cited in DI to support the claim that algorithmic systems require the same governance mechanisms as human processes—ownership, monitoring, escalation. Relevant to CDF’s governance-as-coherence-regulation argument.
Clark, A. (2008). Supersizing the Mind. Oxford University Press.
Extended treatment of the extended mind thesis, arguing that cognitive processes are not confined to the skull but include environmental scaffolds, tools, and social practices. Supports field-theoretic claims that intelligence is a property of brain-body-environment systems, not individual brains.
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7-19.
The original extended mind paper, introducing the “parity principle” (if an external process functions like an internal one, it should count as part of the cognitive system). Foundational for undermining classical computationalism and supporting the relational ontology of field theories.
Dafoe, A. (2018). AI governance: A research agenda. Future of Humanity Institute, University of Oxford.
Systematic research agenda for governing advanced AI, covering verification, alignment, and institutional design. Cited in DI to support the integration of AI into governance frameworks rather than treating AI as an autonomous agent. Relevant to CDF’s analysis of governance-last deployment failures.
Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics. Harvard Business School Press.
A key precursor to DI, arguing that organizations can achieve competitive advantage through analytical decision-making. Establishes the empirical claim that data-driven decision processes outperform intuition-based ones. Cited in DI as part of its intellectual genealogy.
Edelman, G. M. (1987). Neural Darwinism. Basic Books.
Presents the theory of neuronal group selection: neural development and learning involve selective stabilization of synaptic populations rather than instruction. Supports the dynamic reconstruction claim—memory is not stored but recreated through resonant configurations. Cited in field theory literature as evidence against representational storage.
Freeman, W. J. (1994). Neural mechanisms underlying destabilization of cortex by sensory input. Physica D, 75(1-3), 151-164.
Demonstrates that sensory input does not “trigger” fixed responses but destabilizes cortical dynamics, allowing reorganization. Direct empirical support for the claim that perception is active reconstruction, not passive retrieval. Relevant to inference-layer dynamics in CDF.
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127-138.
Proposes that all adaptive systems minimize variational free energy, unifying perception, action, and learning under a single principle. Provides a formal, testable definition of coherence (minimization of surprise or prediction error). Central to operationalizing field-theoretic claims, though organizational translation remains incomplete.
Holland, J. H. (1995). Hidden Order: How Adaptation Builds Complexity. Addison-Wesley.
Foundational text on complex adaptive systems, emphasizing internal models, rule-discovery, and feedback. Cited to highlight DI’s lack of self-organization mechanisms. Directly relevant to CDF’s claim that genuine learning requires attractor reconfiguration, not external redesign.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554-2558.
Introduces Hopfield networks—recurrent neural networks with attractor dynamics that perform content-addressable memory. Demonstrates mathematically how stable patterns (attractors) emerge from collective dynamics. Directly cited in support of attractor dynamics in cognitive systems.
Kauffman, S. A. (1993). The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press.
Develops the NK model of rugged fitness landscapes, showing how systems self-organize into attractors and how adaptation depends on landscape topology. Supports the concept of path dependence and the energy requirement for attractor escape. Central to CDF’s account of organizational transformation.
Kelso, J. A. S. (1995). Dynamic Patterns: The Self-Organization of Brain and Behavior. MIT Press.
Comprehensive treatment of coordination dynamics, demonstrating how behavioral patterns (e.g., finger movements) emerge from nonlinear, self-organizing principles. Provides empirical and mathematical grounding for attractor dynamics as a general systems property.
Konstapel, H. (2026). Toward a unified field theory of mind. constable.blog, April 18, 2026.
The direct source for the field-based theory of mind used in this synthesis. Proposes that consciousness is a coherent, multi-scale relational field rather than a localized neural property. The author is also the author of the present synthesis paper (under a variant first initial J. vs H.—likely a minor inconsistency). Central to the entire argument.
Moser, R. (2026). The Decision-Centric Enterprise: A Manager’s Guide to Decision Intelligence (Draft).
The primary source for DI as presented in the synthesis. Introduces Decision-Dominant Logic, the five-level construct hierarchy, the six dimensions of decision quality, and the governance framework. Cited extensively throughout Sections 2 and 5. The draft status is noted; final publication may update these formulations.
Penrose, R. (1989). The Emperor’s New Mind. Oxford University Press.
Argues that consciousness cannot be captured by classical computation, proposing quantum processes in microtubules as an alternative. Cited in support of quantum approaches to consciousness. Relevant primarily as background to field theory’s anti-computationalist stance.
Penrose, R. (1994). Shadows of the Mind. Oxford University Press.
Extends the arguments of The Emperor’s New Mind, further developing the case for quantum consciousness. Cited alongside Stapp and Vitiello as representative of quantum approaches.
Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. Harvard Business Review, 85(11), 68-76.
Introduces the Cynefin framework, distinguishing simple, complicated, complex, and chaotic domains. Cited to argue that DI’s designability assumption holds in complicated domains but fails under genuine complexity or uncertainty. Central to the critique of DI’s limits.
Stapp, H. P. (2007). Mind, Matter and Quantum Mechanics (3rd ed.). Springer.
Presents a quantum theory of consciousness based on von Neumann’s formalism, emphasizing the role of observation and choice. Cited as part of the quantum approaches to consciousness literature.
Stengers, I. (2010). Cosmopolitics I. University of Minnesota Press.
Whiteheadian process philosophy applied to scientific practices, emphasizing relational ontology and the construction of “well-articulated” assemblages. Supports the field-theoretic claim that intelligence is a property of fields, not isolated agents.
Thompson, E. (2007). Mind in Life: Biology, Phenomenology, and the Sciences of Mind. Harvard University Press.
Comprehensive synthesis of enactive cognitive science, arguing that life and mind share a common autopoietic organization. Foundational for the claim that cognition is world-enactment through organism-environment coupling. Central to field theory’s anti-representationalist stance.
Tononi, G. (2008). Consciousness as integrated information: A provisional manifesto. Biological Bulletin, 215(3), 216-242.
Introduces Integrated Information Theory (IIT), which measures consciousness as Φ—the irreducibility of a system’s cause-effect structure. Provides a formal, mathematically defined measure of coherence (integration) applicable in principle to any system with causal structure. Cited as a potential operationalization of organizational coherence.
Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.
The founding text of enactivism, arguing for the “circular causality” of organism-environment coupling and rejecting representationalism. Central to field theory’s claim that cognition is dynamic enactment, not internal computation.
Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1-17.
Introduces Service-Dominant Logic, shifting value from goods (units of output) to services (processes of co-creation). DI’s Decision-Dominant Logic is presented as a third shift following Goods-Dominant and Service-Dominant logic. Cited to position DI within the evolution of economic logic.
Vitiello, G. (2001). My Double Unveiled: The Dissipative Quantum Model of Brain. John Benjamins.
Presents a quantum field model of the brain based on dissipative dynamics, spontaneous symmetry breaking, and coherent states. Directly cited in support of quantum field approaches to mind. Provides the formal physics background for the term “field” in “field-based theories of mind.”
Weick, K. E. (1993). The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly, 38(4), 628-654.
Classic organizational sensemaking analysis of a wildfire disaster, demonstrating how organizations lose the capacity to interpret ambiguous signals when feedback loops break down. Cited to support the claim that DI’s designability assumption fails under genuine uncertainty. Anticipates decoherence as a failure mode.
Whitehead, A. N. (1929). Process and Reality. Free Press.
The foundational text of process philosophy, arguing that reality consists of events (actual occasions) in process, not static substances. Directly cited as a source for field theories of mind. The synthesis inherits Whiteheadian commitments: decisions are emergent occasions, policies are patterns of process, and coherence is the unity of a multi-scale field.