Bayesian Optimization
Part IX: What Is a Preference?
中文

What Is a Preference?

Every preferential method assumes that the person in the loop has a preference to be found, and that a comparison is a noisy reading of it. Other disciplines have spent a century testing that assumption. Psychology measures how judgments depend on context, attention, and the order of questions, and which famous effects survived replication. Neuroscience studies how value is computed and how long a choice takes. Economics formalized revealed preference and then found people who decline to choose or deliberately randomize. Philosophy asks when it is legitimate for a system to change what someone wants.

The part exists because of the book's argument: the algorithms are mature, and the open questions are about measurement, about what a comparison measures and what asking does to the person (What the book argues). Those questions have been studied for a long time, just not under the name of optimization. Each chapter reports one discipline's evidence, organized around what it tells someone who builds or evaluates a preferential system, and ends with what it implies for modeling a comparison. In several places you can test an effect on yourself.

The part assumes Chapter 16; each chapter stands on its own.

Chapters in this part

  1. 37 Judgment, Decision, and Psychophysics

    What judgment and decision research, psychophysics, mathematical psychology, and the study of heuristics found between 2017 and 2026 about the assumptions behind the comparison likelihood: which famous effects faded in large replications, which held up, and what each implies for modeling a person's answers.

  2. 38 Social, Affective, and Developmental Psychology

    What social, motivational, consumer, emotional, moral, personality, and other branches of psychology found between 2017 and 2026 about stable preferences: which famous effects failed to replicate, why broad dispositions are stable while single choices are not, and what this implies for modeling a person's comparisons.

  3. 39 Neuroscience and Computational Cognitive Science

    What neuroscience and computational cognitive science found between 2017 and 2026 about value in the brain, neural forecasts of preference, response times and attention, efficient coding, active inference, pharmacology, and motor adaptation, and what each implies for modeling a person's comparisons.

  4. 40 Economics, Decision Theory, and Operations Research

    What economics, decision theory, and operations research know about preference that preferential optimization uses or ignores: how large behavioral effects really are, what choices reveal, three sources of random choice, rational inattention, aggregation across people, incomplete preferences, adaptive conjoint designs, stated-preference practice, multi-criteria decision analysis, and interactive multi-objective optimization.

  5. 41 Philosophy and Religious Traditions

    What philosophy and religious traditions say about preference and desire, and what follows for preferential optimization: whether a preference is a mental state or a pattern of choice, when a system may change one, what counts as manipulation, when optimization is the wrong frame, and how Buddhist, Daoist, Confucian, and theological traditions treat desire.

  6. 42 Social Sciences and the Humanities

    Where tastes come from and who gets sampled, what happens when a system that measures preferences also shapes them (performative prediction, recommender feedback, EU rules on manipulative interfaces), what language, institutions, and expertise add, and which widely repeated claims from these fields do not survive a check of the sources.

  7. 43 Natural and Formal Sciences

    What biology, physics, network science, the mathematics of preference, information theory, game theory, and differential privacy tell someone building a PBO system: comparison graphs whose spectrum governs estimation error, a decomposition that measures how much of the data one utility can explain, models of incomplete preference, what the one-bit bound limits, and what privacy would cost.

  8. 44 Design, Sensory Science, Art, and Health

    What design research, architecture, urban planning, engineering design, food and sensory science, empirical aesthetics and music, and the health sciences have learned about eliciting preferences, from placebo pairs and fixation to test-retest benchmarks and validation against real behavior, ending with the methods PBO can adopt directly.

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