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mit/thesis/dual/sdm two candidates

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democratic partial orders

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how we can use co-design to create a participatory system of institutional decision-making

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potential structure

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Thesis: Participatory Decision-Making Using Co-Design and Partial Order

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Claim: Many complex system design problems involve stakeholders with divergent or incommensurable preferences; instead of aiming for total consensus, participatory processes can yield partially ordered sets of solutions that reflect trade-offs transparently and support informed co-evolution.

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Gap

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Most participatory frameworks focus on facilitation and dialogue, but lack formal structures for comparing competing preferences or compositions of solutions.

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Optimization frameworks assume total orders; real decisions often involve incomplete, evolving, or non-comparable preferences.

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Existing design processes rarely show participants the structured space of possibilities or how solutions emerge from trade-offs.

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Hypothesis

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Co-design processes can be formally modeled using partial orders (posets), revealing where preferences align, conflict, or compose.

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This model enables more transparent, explainable, and iterative participatory decision-making.

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Theoretical Foundations

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Category theory: posets as thin categories, functors as structured preference mappings

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Decision theory: partial orders, Pareto frontiers, multi-objective trade-off spaces

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Co-design (Zardini lab, Oikos, Urban Co-Design): participation as structured iteration, not consensus-seeking

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Ethics of participation: transparency and agency arise from structured intelligibility, not necessarily from agreement

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Method Overview

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Model co-design outcomes as elements in a partially ordered solution space

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Define functors that map stakeholder preference structures into solution-space rankings

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Use case study data from participatory workshops (e.g., Oikos, public space planning) to extract preference structures

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Build tooling or visualizations to show emerging posets of design options

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Analyze how changes in stakeholder framing affect the partial order over time

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Expected Contributions

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A formal framework for co-design as participatory ordering, not forced convergence

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Practical tools for participatory planning that reveal structure without reducing complexity

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A generalizable model of participatory reasoning as partial epistemic consensus

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learning as epistomological composition

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knowledge can be represented as relationships between categories of concepts

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potential structure

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Thesis: Understanding Learning as Epistemological Composition and Abstraction

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Claim: Learning is not just information accumulation but a structured, layered process of composing knowledge fragments and abstracting over them; category theory offers a formal language for modeling these nested, compositional epistemic transformations.

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Gap

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Most learning theories are either cognitive (mental models) or statistical (Bayesian update), and rarely formalize how knowledge transforms across abstraction layers.

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Few frameworks exist for representing recursive belief structures, cross-domain analogies, or meta-level learning (learning how to learn).

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There’s little alignment between formal epistemology and applied system learning (design, AI, planning).

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Hypothesis

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Learning can be modeled as composition over structured belief morphisms, with abstraction corresponding to movement up layers of epistemic categories.

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This structure enables analysis of learning loops, reflection, generalization, and design insight.

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Theoretical Foundations

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Category theory: functors, fibrations, 2-categories, composition, pullbacks, abstraction as higher morphisms

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Epistemology: nested belief, revision, speculative reasoning, conceptual change

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Learning theory: curriculum design, analogical transfer, reflection

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System design: modular abstraction, interface design, learning-by-design

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Method Overview

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Define core learning operations (composition, abstraction, analogy) as categorical structures

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Model examples of real-world learning trajectories (e.g. concept maps, planning sequences, reflective practice) using these structures

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Use visual and algebraic tools to reveal where epistemic composition succeeds or breaks

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Optionally build tooling to track or simulate epistemic composition (e.g. a learning graph based on Logseq or Omnicat-style representation)

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Apply the model to system design or planning problems to demonstrate usefulness

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Expected Contributions

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A formal model of learning as epistemic composition across abstraction layers

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Bridging epistemology, design, and cognitive systems

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A novel theoretical foundation for modular, reflective, and cross-domain learning

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