When we think about Artificial Intelligence (AI) and Economics, we might not immediately connect the dots. However, economics plays a fascinating and crucial role in the development and application of AI. We will explore some of the intriguing ways in which Artificial Intelligence and Economics merge together to create intelligent systems. But before that let’s ask three basic questions to ourselves.
- What approach should we follow to align our decision-making process with our personal preferences?
- What strategies can we employ to navigate situations where our personal preferences may not align with those of others while making decisions?
- How can we effectively incorporate our personal preferences into decision-making when the potential rewards or outcomes may be realized in the distant future?
These questions prompted many scholars to reflect upon them. The Science of Economics originated in 1776, when Adam Smith published his influential work, “An Inquiry into the Nature and Causes of the Wealth of Nations.” Smith’s perspective on economics involved analyzing economies as a collective of many individuals, each acting in their own self-interest. However, it is crucial to understand that Smith did not promote or advocate for financial greed as a moral position. To provide a broader context, Smith’s earlier book, “The Theory of Moral Sentiments” (published in 1759), serves as a complementary piece to his economic ideas. In this earlier work, Smith emphasized that individuals possess a natural inclination to care for the well-being of others. He recognized that concern for the welfare of others is a fundamental aspect of every person’s self-interest, rather than a separate and contradictory notion.
Economics is often commonly associated with money, and this association is not entirely unfounded. Many people perceive economics as primarily concerned with financial transactions, wealth accumulation, and monetary systems. In fact, the earliest mathematical analysis of decision-making under uncertain circumstances, Arnauld’s maximum-expected-value formula of 1662, specifically focused on the monetary value of bets. However, it is important to recognize that economics encompasses a much broader scope. While monetary considerations play a significant role, economics also involves the study of how individuals, businesses, and societies allocate limited resources to satisfy their diverse needs and want. It examines the production, distribution, and consumption of goods and services, as well as the factors influencing market behavior, economic growth, and overall welfare. Economics delves into various aspects of human behavior, such as individual decision-making, market dynamics, government policies, and global trade. It explores how incentives, costs, benefits, and risks shape people’s choices and actions. Moreover, economics encompasses the study of how societies address issues like poverty, inequality, environmental sustainability, and the overall well-being of individuals. It provides a framework to understand and analyze a wide range of human behaviors, interactions, and societal phenomena, offering insights into how resources are allocated and the consequences of different economic decisions and policies.
In 1738, Daniel Bernoulli made an important observation about the limitations of the maximum-expected-value formula when applied to larger sums of money, such as investments in maritime trading expeditions. Bernoulli introduced an alternative principle based on the maximization of expected utility, which aimed to better explain human investment choices.
According to Bernoulli’s principle, the marginal utility of money diminishes as individuals acquire more of it. In simpler terms, it suggests that the additional satisfaction or benefit derived from each extra unit of money decreases as a person’s wealth increases. In other words, the value or utility gained from an additional amount of money is not solely determined by its monetary value but also by the individual’s overall wealth and circumstances. This concept of diminishing marginal utility of money implies that people tend to assign higher subjective value to smaller sums of money when they have less of it. As their wealth grows, the incremental value they place on additional money decreases. Consequently, individuals may be willing to take on greater risks or make different investment decisions when dealing with larger sums of money. Bernoulli’s proposition of diminishing marginal utility of money sheds light on the complexity of human decision-making in the realm of investments. It suggests that individuals’ preferences and attitudes towards risk are influenced not only by the expected monetary outcomes but also by their subjective evaluation of utility or satisfaction associated with different financial scenarios. By considering the concept of expected utility, economists can better understand and analyze how individuals make investment choices and assess the potential trade-offs between risks and rewards in different financial contexts.
The Role of Diminishing Marginal Utility in Artificial Intelligence
The concept of diminishing marginal utility, as proposed by Daniel Bernoulli, can be relevant in the field of Artificial Intelligence (AI) when modeling and understanding human decision-making processes. Incorporating this idea into AI systems can help create more realistic and accurate representations of human preferences and choices. In AI, decision-making algorithms often aim to optimize outcomes based on a given objective or utility function. By integrating the concept of diminishing marginal utility, AI systems can better capture the nuanced preferences and behaviors of individuals, particularly in situations involving decision-making processes involving desires and preferences. When AI systems consider the diminishing marginal utility of money, they can account for how individuals may value different amounts of money based on their existing wealth or resources. This understanding can help AI models accurately assess the impact of financial choices on an individual’s overall well-being or utility.
By incorporating Bernoulli’s principle, AI systems can simulate decision-making processes more effectively, particularly in areas such as financial planning, resource allocation, or risk assessment. Such AI models can provide more personalized recommendations or insights by taking into account the diminishing marginal utility of money and the complex interplay between wealth, preferences, and expected utility. In summary, integrating the concept of diminishing marginal utility into AI systems enables a more realistic and comprehensive understanding of human decision-making. This integration can enhance the accuracy and usefulness of AI models in various domains where financial considerations and individual preferences play a crucial role.
You may read more about the development route of Artificial Intelligence (AI) in various fields like AI & Neuroscience, AI & Control Theory, or AI & Mathematics. Or if you want to read more about the intersecting field of AI and Economics, click here. Enjoy reading this article, and consider sharing it with your friends and loved ones to support us. Knowledge sharing is free! Happy Learning!