Posts Tagged “algorithmic logic”

From ‘Content is King’ to GODLIKE, Part 6

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From ‘Content is King’ to GODLIKE, Part 6

In part 5 of “From ‘Content is King’ to Godlike,” we looked at methods and metric used today to predict future outcomes based on contextual control of the environment. In this segment, we look at how Behavioral Economics is impacting rtificial intelligence (AI) algorithms, machine learning, and inferential logic.

What is Behavioral Economics?

Behavioral Economics is the study of human choice and decision-making. Unlike its classical economics predecessor, Behavioral Economics has two key divergences:

  • Whereas classical economic theory assumes that both markets and consumers are rational actors, Behavioral Economics does not. In fact, BE practitioners demonstrate the irony of predictability in human’s behaving badly, or irrationally. 
  • Building from that point, BE has developed a series of scientific methods to test, measure, and yes, predict human irrationality.
  • As the name somewhat suggests, BE bridges the academic disciplines of economics and psychology as well as other social sciences, neuroscience and even mathematics. That’s quite a lot.
  • Incorporates Heuristics, i.e., humans make 95% of their decisions using mental shortcuts or rules of thumb.
  • Incorporates Framing: The collection of anecdotes and stereotypes that make up the mental filters individuals rely on to understand and respond to events.
  • Addresses market inefficiencies that classical economics does not, including mis-pricing and non-rational decision making.

Behavioral Economics is a relatively new academic discipline whose roots go back to the 1960s largely from cognitive psychology. To many, Richard Thaler (Nobel Prize: 2017) is considered the father of BE. The University of Chicago professor has written six books on the subject.

Behavioral Economics Applications

While still considered a fledgling discipline, BE is growing rapidly, and arguably the most consequential new study area in academia. BE has already has been put to practical use in Marketing, Public Policy, and of course, AI, although its presence is rather opaque.

    Principle: Hot-Hand Fallacy—the belief that a person who experiences success with a random event has a greater probability of further success in additional attempts.

Example: In basketball, when players are making shot after shot and feel like they have a “hot hand” and can’t miss.

Relation to BE: Human perception and judgment can be clouded by false signals. There is no “hot hand”—it’s just randomness and luck.

Principle: Self-handicapping—a cognitive strategy where people avoid effort to prevent damage to self-esteem.

Example: In case she does poorly, a student tells her friends she barely reviewed for an exam even though she studied a lot.

Relation to BE: People put obstacles in their own paths (and make it harder for themselves) in order to manage future explanations for why they succeed or fail.

Learn more about Diane Israel. Also, see Diane Israel on LinkedIn.

Principle: Anchoring—the process of planting a thought in a person’s mind that will later influence this person’s actions.

Example: Starbucks differentiated itself from Dunkin’ Donuts through their unique store ambiance and product names. This allowed the company to break the anchor of Dunkin’ prices and charge more.

Relation to BE: You can always expect a grande Starbucks hot coffee ($2.10) to cost more than a medium one from Dunkin ($1.89). Loyal Starbucks consumers are conditioned, and willing, to pay more even though the coffee is more or less the same

Principle: Gambler’s Conceit—an erroneous belief that someone can stop a risky action while still engaging in it.

Example: When a gambler says “I can stop the game when I win” or “I can quit when I want to” at the roulette table or slot machine but doesn’t stop.

Relation to BE: Players are incentivized to keep playing while winning to continue their streak, and to keep playing while losing so they can win back money. The gambler continues to perform risky behavior against what is in this person’s best interest.

Principle: Rationalized Cheating—when individuals rationalize cheating so they do not think of themselves as cheaters or as bad people.

Example: A person is more likely to take pencils or a stapler home from work than the equivalent amount of money in cash.

Relation to BE: People rationalize their behavior by framing it as doing something (in this case, taking) rather than stealing. The willingness to cheat increases as people gain psychological distance from their actions.

These behavioral economics principles have major consequences on how we live our lives. By understanding the impact they have on our behavior, we can actively work to shape our own realities. As such, it’s becoming increasingly difficult, if not impossible, to decouple BE from AI, et al.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at

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