Heuristics
constantly provides, and determine which are valid, useful, real, or relevant; it then computes this information to formulate judgements and make decisions, all requiring a significant input of effort. It must also be noted that any suggestion made by System 1 must have first been believed, and believed as the whole truth, in a phenomenon clunkily referred to as WYSIATI (what you see is all there is), before it can be disbelieved by System 2. This introduces the idea of System 2 as an effort-minimizing tool which sometimes fails to regulate System 1 accurately, or to fully evaluate its suggestions, in order to come to rough judgements so as to avoid extended effortful processes. It should be made clear that, even though it is often presented otherwise, these are not rules or intentional behaviour patterns, but rather consistent, elaborate approximations and mistakes which can prove both insufficient, and highly predictable. The approximate nature of these judgements has quite significant ramifications. Although judgements are quick and can be extremely useful in navigating the constant stream of the day-to-day world, they are also by definition often inaccurate, and are highly ineffective in computing complex issues, even though System 2 has immense problem- solving abilities. The ‘use’ (although they are unconscious and s o cannot be ‘used’) or application of heuristics can lead to altogether faulty or irrational patterns of behaviour, and can provide a skewed vision of the world. Heuristics are also easily manipulated for private ends, used by politicians, supermarkets and governments worldwide to lead people towards voting outcomes, purchases, and behaviours which they would otherwise not adopt for a variety of reasons. This can, from an economics standpoint, lead to significant market failures, be that in the breakdown of the price mechanism, 2 or in the creation of government failure through ineffective market interventionist policy, which fails to accurately predict how consumers will react to corrective measures. Moreover, their misuse by firms has arguably contributed to a rise in globalization through the development of international oligopoly markets, and in turn contributed to the stagnation of international technical innovation. 3
Behavioural microeconomics
Heuristics have, in many ways, been a major thorn in the side of neoliberal economists, and a challenge to the fundamental theory which underlines most of popular economics: supply and demand. Traditional theory holds the assumption that all agents are strictly rational; that is, that they are always maximizing preferences and have full knowledge of both their present and future preferences, and follow a set of principles known as expected utility theory when making decisions. 4 This theory states that, in addition to being entirely rational, agents are also completely selfish, and make decisions based on all the available information, using all of these factors to make a predictive valuation of the payoff of any given decision, of which the best payoff is then always taken. As is relatively intuitive, this theory has problems: people tend to have very little future knowledge of their preferences, or often even current knowledge, and their preferences might even be contradictory. A customer browsing the
2 Hu, Z., Chen, X., & Hu, P. (2016). Technical Note — Dynamic Pricing with Gain-Seeking Reference Price Effects . Operations Research, 64(1), 150 – 157. http://www.jstor.org/stable/24740466. 3 No given author. (2022, January 22). Big tech’s private passions. The Economist. What America’s largest technology firms are investing in | The Economist. 4 Baddeley, M. (2017). Behavioural Economics: A Very Short Introduction) . Oxford University Press.
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