AI-Enabled Analytics for Business. Lawrence S. Maisel

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want or need that influences and impacts our decisions.

      As such, we must first understand how nature has wired us to make decisions before we can appreciate and accept how analytics can contribute to enhancing decision-making that can lead to improved business performance. The need to balance our instinctive judgment with AI for decisions is necessary to fulfill the potential value of analytics in business and avoid the shortcomings associated with traditional decision-making.

      The research of Kahneman and Tversky, who received the Nobel Prize for Economics in 2002, produced a ground-breaking understanding of human judgment and decision-making under uncertainty. Their research is viewed as one of the most influential social science behavioral insights of the past century. It challenged the notion held by many economists that the human mind is unconsciously rational.

      System 2 is invoked for more complex, thoughtful reasoning and is characterized by slower, more rational analysis but is “prone to laziness and fatigue.” If you want to conduct your own experiment along these lines, ask someone to write down the results of a hypothetical sequence of 20 coin flips. Then ask the person to flip a coin 20 times and write down the results. The actual flips will almost certainly contain streaks of only heads or tails—the sorts of streaks that people do not think a random coin produces on its own. This kind of misconception leads us to incorrectly analyze all sorts of situations in business, politics, and everyday life.

      Further, the research of Kahneman and Tversky revealed previously undiscovered patterns of human irrationality: the ways that our minds consistently fool us and the steps we can take, at least some of the time, to avoid being fooled. They used the word heuristics to describe the rules of thumb that often lead people astray.

      One such rule is the halo effect, in which thinking about one positive attribute of a person or thing causes observers to perceive other strengths that are not actually there. For example, a project team was discussing the status of a new marketing campaign. The campaign was led by Billy, who had a reputation for delivering successful campaigns. Team members were asked to give their assessment of progress and, recognizing Billy’s past successes, gave positive evaluations. This reflected the halo effect in that the past successes extended to this project without any factual basis other than Billy’s reputation.

      This work has led to advances in individual behavior. It is full of practical little ideas like “No one ever made a decision because of a number”; Kahneman has said, “They need a story.” Or Tversky’s theory of socializing: because stinginess and generosity are both contagious, and because behaving generously makes you happier, surround yourself with generous people.

      The research has clarified how decisions are made and underlying influences that can impact decisions. These influences are inherent in group interactions and individual biases, which are key to understanding the balance between human judgment and analytics in decision-making.

      Group Decision-Making

      For example, the design team of a successful smartphone is deciding whether to remove the home button on its next version release. The lead designer suggests that the home button be kept, and the decision, after some discussion, is to keep the home button. Later that day, some of the design team meet for lunch, and Peter expresses his preference for removing the home button. Mickey jumps in to say “Me too!” and is followed by Davey. They all acquiesced to the decision since they believed they were the only ones who did not agree. In fact, when the team reassembled, most of the other members also preferred to remove the home button but also did not express their preference.

      For example, a capital project review team is convened to decide on next year’s CapEx budget. Each member is asked to indicate their preferred #1 project. After the first and second members express their preference for the same project, each succeeding member agrees with the first two, although their individual preferences were different. They “go along to get along” and express their agreement with the preferred project.

      These group decision-making processes demonstrate the embedded flaws in human behavior that can produce decisions that lead to under-optimized, inefficient, ineffective, or non-competitive business performance that wastes capital and resources. This punctuates why unbiased, scientific AI and analytics inputs are essential to minimize or eliminate group bias and contribute to improved business performance.

      Individual Bias in Decision-Making

      Individuals think in System 1 (thinking fast), which is the intuitive, “gut reaction” for making decisions. System 2 (thinking slow) is the analytical, “critical thinking” way of making decisions. Most of us identify with System 2 thinking. We consider ourselves rational, analytical beings. Thus, we believe we spend most of our time engaged in System 2 thinking. Actually, we spend almost all of our decision-making engaged in System 1. Only if we encounter something unexpected, or if we make a conscious effort, do we engage System 2.

      System 1 thinking produces various forms of bias; several of the critical modes of bias more recognized by behavioral psychologists are discussed next:

       Inherent bias: One of the biggest problems with System 1 is that it seeks to quickly create a coherent, plausible story—an explanation for what is happening—by relying on associations and memories, pattern-matching, and assumptions. The amount and quality of the data on which the story is based are

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