Overview
In Active Inference, we develop (ensembles of) generative models of ecosystems of shared intelligence by accounting for cognitive system and phenomena. The work of developing generative models is more like doing accounting than doing calculation, memorization, or inference itself – the generative model does the inference for us. One of our functional roles or capacities as an engaged generative modeler, is to take an analytical stance towards accounting for cognitive properties, processes, and perspectives. In this setting, we are the Active AccountAnts. The generative model we create is an Active InferAnt.
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Methods and contributions
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Findings and contributions
- The author argues Active Inference's breadth rests on a first-principles scale-free approach rather than a scheme specific to cognitive systems: it can say more because it says less.
- Building even simple cognitive phenomena (e.g. learning, attention) into the core formulation would, the author argues, restrict the scope of analysis to systems with that character.
- Natural-language descriptors of cognitive phenomena are treated as secondary relational attributions about a realized generative model, not intrinsic aspects of the system.
- The essay suggests the financial–cognitive accounting connection may run deeper than analogy, via category theory, but leaves this to future work.
Methods
- Analogy between financial accounting and generative modeling — The essay frames building Active Inference generative models as accounting rather than calculation, using a spreadsheet/financial accounting situation as the guiding analogy.
- Research vs. Application distinction illustrated with a spreadsheet — Uses the example of validating spreadsheet addition to separate Research questions from question-driven Application.
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Citation
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Catalog details and resources
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