How to Use Decision Making Models for Rational Thinking

Life often presents complicated situations filled with the necessity to make decisions under uncertainty.  In the study of economics, rational decision-makers attempted to optimize outcomes in light of constraints, whether resource, labor, capital, or entrepreneurship.  In practice, this is called constrained optimization. When a person must make a decision, one of the most accepted approaches has been through the application of a rational decision making model.  This decision-making framework provides a structured and sequenced approach to decision-making. Typically, using this approach can help to ensure discipline and consistency factor into the decision-making process.  And as you might guess, the word rational suggests this approach provides logic and order to the decision-making process.  Hence, the rational decision-making model. This post walks through the rational decision-making model’s advantages and disadvantages, how it works, why it helps to improve outcomes, and can serve as a valuable framework for processing information and making a decision.

What is the Rational Decision-Making Model?

---

The rational decision-making model consists of a series of steps, beginning with the identification of a problem or an opportunity for action, processing information and alternatives, and ending with actions taken toward a desired outcome.  By consistently applying a rational decision-making model in your day-to-day life, you can forecast and rely on more predictable outcomes from your decisions. Further, because your decisions rely on rational thinking, the rational model of decision making assumes people will make choices which maximize the benefits and minimize any costs. The idea of rational choice holds immense weight in economic theory and underpins the decisions predicted through behavioral modeling. One assumption used in economic theory, which does not always apply in the real world, is that of perfect information on which to base the choice. For example, when making a decision on which renters insurance or landlord insurance policy covers their needs best, the rational decision making model assumes this person has perfect information of all policies available in the market and makes a fully-informed decision best suited to their needs.  Often, we know this not to be true and instead individual actors within a rational decision making model decide their preferred outcome with imperfect information. To overcome this information incompleteness preventing the best decision from being reached, best practices include using data, analysis and statistics (all of which have ‘fat tailed’ distributions) to become fat and happy in life.  Fat tailed meaning perfect outcomes happen more often because they occur closer to the average of a normal distribution.

What Does Decision-Making Research Show?

---

Apparently, academic-level research tends to show humans as poor decision-makers.  According to a Harvard Business School study, people can easily be persuaded by models, or those tools which make an interpretation of known data, whether informational or misleading. For example, when a young couple begins looking at houses after wanting to quit deciding between living in a condo vs. apartment, they decide to meet with a realtor.  In reviewing homes in a desired area, the realtor quips, “Home prices in this neighborhood are rising because of the schools.”  Instantly, the realtor has provided a model for rationalizing the rise in prices, saying the homeowners-to-be should pay attention to local schools, because they serve as an important determinant of house price appreciation. Potential Democratic Presidential candidates who perform poorly in the Iowa caucuses often point to wealthy donors for the New Hampshire primary saying, “They pick corn in Iowa and presidents in New Hampshire.” This suggests Iowa caucus results should not figure into donors’ minds when determining which candidate will ultimately meet success in the general election campaign.  If history is any guide, they should ignore this conclusion, unless the race involves Republican hopefuls. In these instances, an example lays out a case using data available to the audience, though the key persuasive element is not the information itself.  Rather, it is that the expert highlights a relationship between outcomes and data in a way that logically leads the audience to take an action the expert offers. Said formally, the Harvard Business School study concludes when experts propose a model to suggest how to organize past information (e.g., investment performance, campaign results, and home price trends) to make predictions (e.g., about future returns, results, and market movements), model users find them more compelling when they create a relationship between data and an outcome understood by the user.  Model users also tend to favor those models which better explain the past. Humans have a predisposition to overfit past actions to future events.  While the past often serves as the best predictor of the future, it does not necessarily work for all situations.  Whereas sometimes, past performance should weigh heavily in the mind. This kind of persuasion used by models is ubiquitous.  In the world of finance, when recent market performance is better than long-term averages, bullish day traders use this as a signal to claim “this time is different.”  In debating climate change, one side might argue that extreme weather events provide evidence of global warming and the need for more incentives for solar energy, while the other might argue they reflect “noise” in an inherently unpredictable process. Models use persuasion and economists’ understanding of persuasion has typically focused on the disclosure of information rather than its interpretation. The Harvard study finds humans, when presented with a true model for predicting an event’s occurrence, often still select the wrong model’s predictions because it better fits the past and the views of those who presented the model.  Why? Because people anchor to their unconscious biases and use publicly available data, information, and other references to confirm what they already believe based on past events and understanding. With all this background on the persuasiveness of models, what can we rely on for making a sound decision based on solid facts and figures which accurately represent reality?  If you guessed I’d say a rational decision-making model, you’d be correct. Odd, after I just panned models above, correct? The difference between those erroneous models and the rational decision making model we will review below is this one relies on rational, objective decision-making and disregards anchoring, affirmation, or confirmation biases.

Related: 15 Leadership Principles to Become a Successful Leader

A General 8-Step Rational Decision Making Model

---

Rational decision making processes consist of a sequence of steps designed to develop a desired solution rationally.  Typically these steps involve:

1. Identifying a Problem or Opportunity. First, you must recognize a problem or see worthwhile opportunities.  A rational decision making model works best when employed where relatively complex decisions must be made.  You should first decide if you have a problem to solve or a decision to make.
2. Define the Desired Outcome. Walk through the problem and define your desired outcome you would like to see happen. This requires knowing your goals and objectives before you consider the route to accomplish them. Where possible, you should consider aligning with the strategic, tactical and operational levels of the organization or decision-making body.
3. Gathering Relevant Information. Second, you must decide what information has relevancy for this decision.  Isolate what you need to know before making a decision and what will help with guiding you toward the right decision.
4. Analyzing the Situation. Does your circumstance have multiple potential courses of action available to you?  If so, what different interpretations of the relevant information gathered in step two apply?  Review a list of structured questions (i.e., who, what, when, where, why and how) to consider a broad and deep analysis of your circumstance or problem.
5. Developing Options. Your next step involves developing several possible options with creativity and effectiveness in mind.
6. Evaluating Options. Decide the most important criteria for evaluation and weigh each option carefully and deliberately.  Identify the potential outcomes of each option and how it impacts your circumstance.
7. Selecting a Preferred Alternative (Aware of Opportunity Costs). Consider each option with awareness of the opportunity costs of each.  In other words, what is your second best option in comparison to your first?  If you have option #1 in mind, what could you otherwise decide with option #2?  This is your opportunity cost.
8. Acting on the Decision. Once you’ve identified your problem or opportunity, gathered relevant information, analyzed the situation, developed and evaluated options, and selected your preferred alternative out of many, you must now act on the decision.  Show commitment to your decision. Does it have the support of your colleagues?  Are they committed to making the decision work for your team?

Assumptions of the Rational Decision-Making Model

---

The rational decision making model assumes people make choices which maximizes benefits while minimizing costs.  As discussed previously, the model relies on the assumption people possess perfect information and therefore awareness of all relevant information and alternatives.  In practice, we know this does not always happen. The rational model further assumes:

• Measurable criteria exist for which data can be collected and analyzed
• An individual possesses the cognitive ability, time and resources to evaluate each piece of information and alternative against the others in an objective manner

The rational decision-making model does not consider factors which we cannot quantify.  In other words, the ethical concerns of a decision. Further, it ignores the weight of personal feelings, relationships, or agreements.  Stated succinctly, the rational decision-making model values facts, data and analysis over intuition or desires. As a result, relying solely on a rational decision-making model becomes impractical for most and instead should serve as a guide for leading you to the best choice within your constraints.  In other words, make the optimal choice in the presence of constraints. This leads to a brief discussion on the alternatives of rational decision-making.

Alternatives to the Rational Decision-Making Model

• Bounded Rationality. Based on my experience in the field of economics, many of my colleagues have long expressed a desire to explain exactly why humans can sometimes act counter to pure economic rationality – or even someone’s own self-interest.  The bounded rational decision making model holds that humans make decisions based on limited information, including the cognitive and rational limits of their mind. For example, the finite amount of time available for making certain decisions.  Because humans lack the ability and resources to arrive at the optimal solution given resource constraints, they instead apply their rationality to a set of choices within their understanding and opportunity set. These represent a narrowed down set of options by virtue of the absence of complete information and boundless resources.
• Prospect Theory.  Relying on heavyweights in the decision science field, Amos Tversky and Daniel Kahneman developed an alternative to rational decision-making models called Prospect Theory.  The concept reflects the revealed (empirical) preferences that, contrary to rational choice theory (aka rational decision-making model), people fear losses more than they value gains.  As a result, they tend to weigh negative outcomes more heavily than their actual potential cost.  In all likelihood, if human wiring differed, we wouldn’t be here. To illustrate this theory, studies by these famous academic show people would rather accept a deal offering a 50% probability of gaining $200 over one which has a probability of losing $100.

Both of these rely on elements outside a rational decision-making model based on perfect information.  Humans have emotional responses rooted in evolutionary development which have allowed for the propagation of our species through time. Thinking humans can rely on perfectly rational logic when evaluating a decision would be foolhardy.  However, when evaluating the ability for humans to make decisions, it is important to consider one end of the spectrum as being entirely rational and something to which humans should aspire should they have the desire for making perfectly rationed decisions.  The other end is irrationality and based entirely on emotion or devoid of factual considerations. Perhaps a better alternative is one which incorporates imperfect information, proneness to human emotion, and flaws in human persuasiveness from unconscious biases. Instead, we should favor a robust decision making model which uses a particular set of methods and tools developed through human evolution to make decisions under deep uncertainty.  Using a deliberate process for developing alternatives and taking into account new and known information might show a better outcome for us to follow.