Citizens, Madmen and Children: Equality, Uncertainty, Freedom and the Definition of State.
Bibliographic record
Abstract
In this paper, I argue that a fundamental tradeoff between freedom and equality in human social organization is, in fact, a learnable schedule for the management of uncertainty for an individual agent operating in a social world pervaded with randomness. This implies that political organization and state structures may be viewed as different ways of handling uncertainty, each trading off freedom and equality in a slightly different manner, but each with an equivalent, and theoretically symmetric, balance between the two. I will argue that uncertainty in an intelligent agent arises from one of three primary sources. First, the agent’s logical, objective, mental model of the environment may be noisy. Second, subjective, affective appraisals, of other individuals, their behaviours, and their intentions may present substantial randomness. Finally, the relationship between the objective, real, external world, to the subjective, internal world of the agent may be blurry. As I will show, in any basic two-level Bayesian model of reasoning, these same three sources of uncertainty naturally arise as three learnable parameters. These three parameters govern the operation of the model, but must be traded-off against each other. Furthermore, I will give these three dimensions of uncertainty some theoretical validity, by showing how they pervasively occur across a wide swatch of literatures as three dimensions of political freedom and equality. The relationship between the parameters and the freedoms is a key step in gaining a better understanding of political movements. For example, I discuss how the transition from mechanical solidarity to organic solidarity during the industrial revolution in the 19th century, as described by Durkheim, is in fact a shift in the management of uncertainty to one focused on immediate meanings to one focused on trust. Further, such shifts may be purposefully initiated by politically interested groups, by spreading new narratives or introducing new policy. Viewed as uncertainty management, any shifts so induced should be carefully evaluated in terms of what freedoms it compromises, with smoother transitions assured for shifts that remain in a position of equilibrium with respect to freedom and equality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".