Heuristics and Public Policy: Decision Making Under Bounded Rationality

Abstract

How do human beings make decisions when, as the evidence indicates, the assumptions of the Bayesian rationality approach in economics do not hold? Do human beings optimize, or can they? Several decades of research have shown that people possess a toolkit of heuristics to make decisions under certainty, risk, subjective uncertainty, and true uncertainty (or Knightian uncertainty). We outline recent advances in knowledge about the use of heuristics and departures from Bayesian rationality, with particular emphasis on growing formalization of those departures, which add necessary precision. We also explore the relationship between bounded rationality and libertarian paternalism, or nudges, and show that some recent objections, founded on psychological work on the usefulness of certain heuristics, are based on serious misunderstandings.

Institutionalization of Behavioral Regulation: Foreign Experience

“…На початку 2018 р. К. Санстейн зі співавторами зробили важливі кроки по формалізації підходу до опису евристик і когнітивних помилок: уперше більшість описаних представниками поведінкової економіки відхилень було викладено в математичних термінах [9], а також були обговорені питання щодо можливості оцінки вповноваженими органами потенційних вигод і втрат від застосування інформаційного підштовхування. О станнім часом популярність застосування поведінкової теорії у вигляді «підштовхування» як способу впливу на суб'єктів господарювання в процесі прийняття ними рішень відчутно зросла: майже 150 країн використовують подібні технології.…”

Heuristics and Public Policy: Decision-making Under Bounded Rationality

“…In this section, we give a simple taxonomy of situations that have played an important part in the debate on the appropriate heuristics to use. 5 5 For a more formal treatment, see Dhami, al-Nowaihi, & Sunstein (2018). …”

“…For instance, when asked to write down a random sequence of coin tosses, subjects produce too much negative autocorreation, that is, alternate too much between heads ( H ) and tails ( T ). So randomly generated sequences by humans have too much autocorrelation; the sequence H , H , H , H is very likely to be followed by T , although the a-priori chance of a H or a T is identical; see Dhami, al-Nowaihi, & Sunstein (2018) for the relevant references.…”

“…2 2 In this paper, we formally define some of the main heuristics. For a full set of definitions, see Dhami, al-Nowaihi, & Sunstein (2018). Many of the objections to the KT&O programme can be successfully addressed with suitable modifications and a fuller consideration of the empirical evidence (see the section ‘An Evaluation of the Criticisms of the KT&T Programme’). One of the most common objections is that the biases in the KT&O programme are washed away if one uses a frequency format rather than a probability format.…”

“… 2 In this paper, we formally define some of the main heuristics. For a full set of definitions, see Dhami, al-Nowaihi, & Sunstein (2018). …”