Subjective Coherence: A Novel Framework for Understanding Local Consistency and Global Inconsistency in Human Cognition and Artificial Intelligence
Abstract
This paper introduces the novel concept of subjective coherence, presenting a paradigm-shifting framework that reconciles the paradox of locally consistent yet globally inconsistent decision-making in human cognition. Extending beyond traditional bounded rationality theories, this framework offers a more nuanced understanding of human decision processes. We explore the implications of subjective coherence for artificial intelligence (AI) development, arguing that integrating this concept into AI design represents a significant advancement toward creating systems capable of navigating complex, dynamic environments with human-like flexibility and robustness.
1. Introduction
Classical decision theories have long posited that rational agents exhibit consistent, transitive preferences, leading to globally coherent decision-making patterns. However, empirical observations reveal that human decision-making often displays context-dependent choices, challenging these traditional notions. This paper introduces the novel concept of subjective coherence, a theoretical framework that reconciles the apparent contradiction between local consistency and global inconsistency in human cognition.
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The novelty of our approach lies in its departure from traditional models of rationality and bounded rationality. While previous theories have acknowledged limitations in human decision-making, they have generally treated inconsistencies as deviations from an ideal rational model. In contrast, our framework posits that these "inconsistencies" are fundamental features of adaptive cognition, enabling flexible responses to complex, dynamic environments.
2. Core Concepts and Definitions
2.1 Local Consistency
Local consistency is defined as the optimization of decisions within a specific context, taking into account immediate environmental constraints and cognitive limitations. This concept transcends simple context-dependence by encompassing the notion that decisions are locally rational given the current cognitive and environmental state.
2.2 Global Inconsistency
Global inconsistency refers to the emergence of contradictions or violations of transitive preferences when examining a series of locally consistent decisions across different contexts or time points. This phenomenon reflects the dynamic nature of human preferences and the influence of changing contexts on decision-making processes.
2.3 Subjective Coherence
We introduce subjective coherence as a novel theoretical construct that describes the perceived overall coherence in decision-making, despite the presence of global inconsistencies. Subjective coherence captures the individual's sense of logical consistency in their choices, even when analyzed from an external, global perspective.
2.4 Non-Transitive Preferences
Non-transitive preferences are defined as preference orderings that violate the transitivity axiom of classical rationality (i.e., if A > B and B > C, then A > C does not necessarily hold). Our framework posits that non-transitive preferences are not merely errors or biases but can serve adaptive functions in complex decision environments.
2.5 Examples
2.5.1 Color Preference: Consider the preferences: Blue > Green, Green > Red, Red > Blue. These preferences may be based on different contextual motivations such as mood, situational demands, or environmental influences, leading to non-transitive ordering.
2.5.2 Food Preference: Another example includes preferences like Salad > Pizza, Pizza > Sushi, Sushi > Salad, reflecting varying contexts.
3. Theoretical Foundations
3.1 Beyond Bounded Rationality
While our framework builds the concept of bounded rationality, we argue that subjective coherence represents a significant theoretical advancement. Unlike bounded rationality, which often treats cognitive limitations as constraints on an ideal rational process, subjective coherence posits that local consistency coupled with global inconsistency is an adaptive feature of human cognition. This enables flexible responses to complex, dynamic environments, enhancing overall decision-making effectiveness.
3.2 Cognitive Dissonance and Subjective Coherence
We propose a novel integration of cognitive dissonance theory with subjective coherence, highlighting their differences and complementary roles. Cognitive dissonance drives individuals to resolve discomfort from contradictory beliefs to restore consistency. In contrast, subjective coherence views local inconsistencies as adaptive, focusing on maintaining a coherent self-view without resolving every contradiction. This approach emphasizes flexibility, while cognitive dissonance emphasizes resolving conflicts to achieve psychological comfort.
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4. Implications for Artificial Intelligence
4.1 Limitations of Classical AI Systems
Traditional AI systems often aim for global consistency and logical coherence. This approach may limit adaptability and fail to capture the nuanced, context-dependent nature of human decision-making. Such systems can become rigid, struggling to operate effectively in dynamic and unpredictable environments where flexibility is paramount.
4.2 Integrating Subjective Coherence into AI
We suggest modifications to existing AI architectures that allow for the evolution of preferences over time and across contexts.
We are conceptualizing choices as nodes in a dynamic decision network. By mapping decision-making moments as interconnected nodes, we can examine the flow of decisions over time and across varying contexts, highlighting how local rationality contributes to overall subjective coherence despite global inconsistencies.
By embedding subjective coherence principles, AI systems can better navigate complex environments by prioritizing local consistency while managing global inconsistencies. This leads to more robust and flexible AI agents capable of handling real-world complexities.
5. Conclusion
This paper introduces subjective coherence as a novel framework for understanding human decision-making and advancing artificial intelligence. By reconciling local consistency with global inconsistency, our approach offers a more nuanced and realistic model of cognition. Future research should explore empirical validations of subjective coherence in diverse decision-making scenarios and further investigate its applications in AI system design to fully realize its transformative potential.