Overview
The scientific process plays out in a multi-scale system comprising subsystems, each with their own properties and dynamics. For the practice of science to generate useful world models—and lead to the development of enabling technologies—practicing scientists, their theories, methods, dissemination, and infrastructure (e.g., funding and laboratories) must all fit together in an orchestrated manner. Scientific practice has broad societal implications that go beyond mere scientific progress: we base our decisions on theoretical (i.e., models and forecasts) and technological (e.g., vaccines and smartphones) scientific advances. This paper applies the free energy principle to provide a multi-scale description of science understood as evidence-seeking processes in a nested hierarchy of living (biological and behavioural) and epistemic (linguistic) structures. This allows us to naturalise the scientific process—as distributed self-evidencing—in terms of dynamics that can be read as inference or Bayesian belief updating; i.e., processes that maximize the evidence for a generative model of the sensed and measured world. The ensuing meta-theoretical approach dispels the notion of science as truth-pointing and foregrounds inference to the best explanation—as evinced by the beliefs of scientists and their encultured niche. Crucially, it furnishes a way of simulating the practice of science, which may have a foundational role in the next generation of augmented intelligence systems. Epistemologically, it also addresses some key questions; e.g., is science a special? And in what ways is scientific pursuit an existential imperative for all beings? These questions may be foundational in how we use and design intelligent systems.
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Methods and contributions
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Findings and contributions
- The authors claim science can be described as a higher-level ensemble of Markov-blanketed systems that constrains and is constrained by its parts.
- They argue the approach naturalises science as distributed self-evidencing, foregrounding inference to the best explanation over truth-pointing.
- They propose scientific practice as collective inference on shared generative models, yielding persistent entities: the scientific community and cultural technologies.
- They argue framing science as nested active inference implies a shift from falsificationism to evidence-based model comparison.
- As future work, they claim an FEP-based computational model of scientific cognition could be used to predict via simulation which interactions and constraints promote higher-level intelligence.
Methods
- Free energy principle applied as a multi-scale description of science — Uses the FEP and active inference to describe science as evidence-seeking processes in a nested hierarchy of living and epistemic structures.
- Two-scale analysis: individual and collective scientific cognition — Organizes the account into individual-level scientific cognition (Section 2) and collective, institutional scientific cognition (Section 3), treated as hierarchically integrated.
- Review of Thagard's ECHO explanatory-coherence model — Discusses Thagard's connectionist ECHO model as a prior computational model of individual scientific cognition.
- Synthesis of distributed cognition and niche construction under the FEP — Draws on distributed cognition, Latour's actor-networks and Giere to argue Bayesian mechanics and the FEP can formalize distributed scientific practice.
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