Utility Theory and Decision Analysis

Utility theory in consumer behavior studies states that consumers have a rank order of preferences for items in their choice range. This theory is relevant to psychology and economics as it concerns individual behavior and interaction with the market.

Utility Theory as a Positive Theory

This theory can be called a positive theory within economics. Positive theories are concerned with 'how things are,' as opposed to normative theories, which focus on 'how things should be.' Thus utility theory talks about how consumers act rather than how they should. This theory can be represented analytically using a utility function or a mathematical formulation that ranks the individual's preferences in terms of satisfaction different consumption bundles provide. Thus, under the assumptions of utility theory, we can assume that people behaved as if they had a utility function and acted according to it.

Ordinality of Utility Theory

When faced with a purchase decision, the consumer must choose from bundles of options. The theory propounds that items within these bundles are ranked from 'most preferred' to 'least preferred' and thus assume a preference gradient. This indicates that these items have ordinal utility as opposed to the cardinal utility, which states that items hold values regardless of the context and conditions in which they exist.

Assumptions of Utility Theory

The theory has several underlying assumptions that are important to understand to measure utility accurately.

  • The first assumption is that individuals have a consistent and transitive preference. This means that individuals' preferences remain constant over time, and they can rank different options logically. For example, if an individual prefers option A over option B and option B over option C, they must prefer option A over option C.

  • The second assumption is that individuals have complete information about their options. This means that individuals have all the necessary information about each option and can make informed decisions. In reality, this assumption is often violated as individuals need complete information about the goods and services they are considering.

  • The third assumption is that individuals are rational and make decisions that maximize their overall satisfaction. This means that individuals make decisions that increase their overall happiness and well-being. In reality, individuals may make decisions that are not in line with their preferences or may not fully understand them.

  • The fourth assumption is that individuals have a continuous and differentiable utility function. This means that the satisfaction individuals derive from consuming a good or service can be measured, and the rate of change can be calculated. In reality, individuals may not be able to measure their satisfaction accurately, and the relationship between consumption and satisfaction may not be continuous or differentiable.

Decision Analysis

Decision analysis is a field within psychology that focuses on understanding how individuals make decisions. It involves using quantitative methods to examine how people choose between different options and understand the factors influencing their decisions. Decision analysis has become increasingly important in recent years, as many important decisions are made in complex and uncertain environments.

Decisions Under Uncertainty

One of the central concepts in decision analysis is the concept of decision-making under uncertainty. This refers to situations in which the outcomes of different options are uncertain. In these situations, individuals must use their best judgment and make decisions based on their preferences and available information. Several theories declare cognitive load and uncertainty as important decision-making factors. It has been found that under stress, there is a greater tendency to make decisions using heuristics or mental rules of thumb. Other theories also indicate that social decision-making or attribution is altered under high cognitive load, as stated by the Elaboration Likelihood Model of social psychology.

Normative and Descriptive Decision-Making

Another important concept in decision analysis is the distinction between normative and descriptive decision-making. Normative decision-making involves using mathematical models and algorithms to determine the best decision in a given situation. On the other hand, descriptive decision-making involves studying how people make decisions in real-world situations. Decision analysis also involves using various tools and techniques to understand decision-making. For example, decision trees are commonly used to represent the options available to individuals and help them understand their decisions' consequences. Probabilistic models, such as Bayesian networks, can also represent the uncertainty surrounding different options.

Application of Decision Analysis

In addition to its practical applications, decision analysis has contributed to our understanding of human decision-making. For example, research in decision analysis has shown that people often use simple heuristics, or shortcuts, when making decisions. This has led to a deeper understanding of the biases and errors that can occur in decision-making. Understanding the decision-making process is crucial in understanding our blindspots when making choices in our daily lives. It is useful in developing patterns in which consumers choose and buy products that help the market curtail better to their needs.

Investor Utility Theory

Utility measures how satisfied people are with various bundles of goods and services. In the case of investments, an investor's contentment with a portfolio is determined by the assets in which the investor decides to invest. In the example above of a risk-averse individual, the utility of receiving Rs.75 from a sure outcome is greater than the utility of receiving Rs. 90 through a bet. Because different people have varying risk preferences, not all risk-averse people will rate investment possibilities similarly. Consider the case where the expected value of a bet is Rs. 50. If the assured outcome is Rs. 50, all risk-averse people will prefer the guaranteed outcome over the gamble. If the outcome is less than Rs. 50, say Rs. 35, some risk-averse investors may take it, while others may reject it. Others may be undecided between a fixed sum of Rs.35 and an unknown amount of Rs.50.

Assuming that investors are risk-averse, they always prefer higher returns to lower returns. They are rational because they have consistent preferences and can rate alternative portfolios in their preferred order. The preferences are transitive, which means that if an investor favors portfolio X over portfolio Y and portfolio Y over portfolio Z, he must also prefer portfolio X over portfolio Z. As a result, the indifference curves for the same investor will never touch or collide. Consider the utility function of a risk-averse investor, which is as follows −

$$\mathrm{𝑈 =\mu-\frac{1}{2}(A^{*}\sigma^{2})}$$

where U is an investment's utility, $\mathrm{\mu}$ the expected return, and $\mathrm{\sigma^{2}}$ is the investment's variance. A is a risk aversion metric that quantifies the additional incentive an investor needs to accept marginal risk. Risk-averse investors would want a bigger reward for taking on extra risk. As a result, the value of A would be greater for such persons. On the other hand, a risk taker will have a lower A since they maximize both risk and reward. As a result, we have the following options.

A > 0; the investor is risk-averse, i.e., higher risk reduces utility

A = 0; the investor is risk neutral, i.e., an increase in risk has no impact on the utility

A < 0; the investor is a risk seeker i.e., the higher risk increases utility

Note that a risk-free asset whose variance is zero (${\sigma^{2}=0}$) generates the same utility for all investors. Note also the following for the above utility function.

  • Utility for investors can be highly positive or highly negative. Hence, it is unbounded on both sides.

  • Higher return gives higher utility.

  • The greater the volatility in the investment, the lower the utility. The decrease in utility, however, will be greater than the change in variance since utility is multiplied by the risk aversion coefficient A.

  • Utility is only relevant in ranking different portfolios, and it does not assess satisfaction directly. For example, a portfolio X with a utility of 9 is only sometimes three times better than a portfolio Y with a value of 3. With a utility of 9, portfolio X might boost our happiness 20 times or just a little. However, investors prefer a portfolio with a utility X of 9 over a portfolio Y with a utility of 3.


Utility theory and decision analysis are fields within psychology that focus on understanding how individuals make decisions in complex and uncertain environments. It involves using quantitative methods and tools to understand the factors influencing decision-making and individuals' decision-making processes. Decision analysis has important practical applications and contributes to our understanding of human decision-making.

Updated on: 03-Mar-2023

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