ASRJC Theme 1 Rationality in Decision-Making Lecture notes (Final) Printed

THEME 1: Rationality in Decision-Making Anderson Serangoon Junior College Economics Department A 1 THEME 1: RATIONALITY IN DECISION-MAKING CHAPTER 1: RATIONALITY AND BOUNDED RATIONALITY IN DECISION MAKING 1. THINKING SKILLS FOR H3 ECONOMICS 2. RATIONAL DECISION-MAKING, SHADOW PRICING AND DISCOUNTING 3. BOUNDED RATIONALITY A. LOSS AVERSION • ENDOWMENT EFFECT • SUNK COST FALLACY • STATUS QUO BIAS B. SALIENCE BIAS 4. BOUNDED WILL-POWER & BOUNDED SELF-INTEREST 5. APPLICATION OF THE USE OF BOUNDED RATIONALITY, BOUNDED WILL-POWER & BOUNDED SELF-INTEREST BY ECONOMIC AGENTS • NUDGE THEORY Anderson Serangoon Junior College JC2 H3 Economics 2025

THEME 1: Rationality in Decision-Making Anderson Serangoon Junior College Economics Department A 2 THEME 1: RATIONALITY IN DECISION-MAKING SYLLABUS CONTENT (H3)

THEME 1: Rationality in Decision-Making Anderson Serangoon Junior College Economics Department A 3 THINKING SKILLS FOR H3 ECONOMICS 1.1 Introduction In the study of H3 Economics, students often engage in independent research and would need to critically evaluate different references to develop an in-depth understanding of the topics. In a world where information is readily available for economic agents, an understanding of the pitfalls in the use of information is crucial for economic agents to refine their decision-making skills, to avoid making decisions that may seem irrational on hindsight. It is important for students of H3 Economics to develop an awareness of statistical limitations (misleading comparisons and selection bias), the plausibility of the ceteris paribus assumption and common logical fallacies (fallacy of composition, post hoc fallacy and conjunction fallacy) to critically evaluate the relevance and validity of information given; economic principles, concepts and theories; and perspectives and decisions made by economic agents. As students analyse information, here are some important questions that they should consider: • Economists frequently stress the assumption of ceteris paribus ʹ to what extent is this important and how feasible is this in the analysis of economic events? • What are some logical fallacies that economic agents can fall prey to? • What are some common pitfalls in the use of statistics? • How can economic agents take advantage of appropriate and careful use of information to enhance their decision-making? As they consider the questions listed above, it is also important for students of H3 Economics to consider and understand the nature and significance of Economics as a social science (as compared to the natural sciences) and the foundations of economic analysis (model, evidence and statistical anal 1.2 Plausibility of Assumptions Ceteris paribus assumption: In Economics we often assume ceteris paribus (all other factors remain constant) when we study two or more variables of interest. When economic agents attempt to predict the outcomes of their decisions, knowledge that many other factors can simultaneously affect these outcomes is very important. As such, in establishing the impact of a decision, economic agents should seek to collect information on how various external elements can affect the outcome of a decision, and ensure that these elements are accounted for when predicting the outcome of the decision. However, by virtue of Economics being a social science, even with the use of advanced statistical software, it is not possible to hold all other factors constant. Despite this, the ceteris paribus assumption remains a useful abstraction that helps us simplify and understand social reality. 1.2.1 Common logical fallacies Because economic decision-making often involves logical reasoning, understanding common logical fallacies enables agents to escape some of these common pitfalls. However, it is

THEME 1: Rationality in Decision-Making Anderson Serangoon Junior College Economics Department A 4 unfortunate that logical fallacies are prevalent in popular economic discussions in the news media and elsewhere. As such it is important to be aware of these fallacies so that we can identify invalid arguments when we see them. a. Fallacy of Composition A fallacy of composition occurs when we conclude that what is true for a part is true for the whole (Case and Fair, 2004, p. 11). However, in actual fact, what is true for a part is not necessarily true for the whole. An individual spectator at a soccer match can see better by standing up while the rest remain seated. But if everyone stands, no one can see better. b. Post hoc Fallacy A post hoc fallacy occurs when we erroneously conclude that since Event A happens before Event B, Event A caused Event B to occur (Case and Fair, 2004, p. 11). This train of thought is based solely on the order of events that happened, rather than to consider other potential reasons. As an example, suppose a firm decreases its prices and notices that sales of its goods decrease during the period of the price decrease. It thus concludes that the Law of Demand is false, and the price fall has caused the fall in quantity demanded of its products. This would be an example of the post hoc fallacy, if concurrent to the decrease in prices, there was a fall in income or tastes and preferences for firms’s product, which overshadowed the positive effect of the fall in prices, causing sales to fall as a result. c. Conjunction Fallacy The conjunction fallacy occurs when people judge a conjunction of two events to be more probable than one of the events in a direct comparison. For example, if someone tells you that Jack went to a theme park with his daughter and rode the roller coaster, is Jack more likely to be just a man, or is Jack more likely to be both a man and also a thrill seeker? Most people would pick the latter based on the assumption that if Jack took the roller coaster, he must also be a thrill seeker even though the probability of two events is smaller than the probability of the single event, as illustrated in the Venn diagram in Figure A below. Figure A: Conjunction in a Venn Diagram

THEME 1: Rationality in Decision-Making Anderson Serangoon Junior College Economics Department A 5 In an experimental study by Kahneman and Tversky (1983), evidence showed that people do fall prey to the conjunction fallacy. Tversky and Kahneman (1983) used this example to illustrate conjunction fallacy: Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti- nuclear demonstrations. Which is more probable? 1. Linda is a bank teller. 2. Linda is a bank teller and is active in the feminist movement. Interestingly, in repeated experiments by the authors, between 80% and 90% of respondents commonly choose option 2. This is Event C in the Venn diagram in Figure A above, with ͞bank teller͟ as Event A and ͞feminist movement͟ as Event B. From the Venn diagram, there is no way the overlap or conjunction of Events A and B can be bigger than Event A, yet a rich description of Linda in the example above seem to make people neglect this simple fact. 1.3 Statistical Limitations Economists often use statistical analysis to draw inferences from raw economic data. The specific field where statistical methods are applied to Economics is called econometrics. It is thus important to understand some statistical fallacies so that we can correctly interpret statistical evidence. 1.3.1 Misleading Comparisons A misleading comparison occurs when we compare two or more things in a way that does not reflect their true differences (Stockman, 1999, p. 26). A very important example of misle