How does Decision Science help Data Scientists, Researchers, and Managers?
Data-Driven Decision-Making with Decision Science
In this article, I have tried to share my knowledge related to Decision Science in a simplified way for Managers, Researchers, Analysts, Scientists, and Students who want to implement these approaches in their work process.
What is a Decision?
A choice that you make about something after thinking about it.
What is Decision Making?
Decision-making is the process of selecting a belief or a course of action among several possible alternative options.
Types of Decision-Making Approaches
1. Realist (Expected Value)
Realists compute the expected value under each action and then pick the action with the largest expected value. This is the only approach of the four that incorporates the probabilities of the states of nature. The expected value criterion is also called the Bayesian principle.
2. Optimist (Maximax)
The optimist looks at the best that could happen under each action and then chooses the action with the largest value. They assume that they will get the most possible and then they take the action with the best-case scenario. The maximum of the maximums or the “best of the best”.
This approach looks positive and sounds like it will result in maximum profit but it lacks pragmatism. So the Risk is high.
Example: This is the lottery player; they see large payoffs and ignore the probabilities.
3. Pessimist (Maximin)
The pessimist looks at the worst that could happen under each action and then choose the action with the largest payoff. They assume that the worst that can happen will, and then they take the action with the best worst-case scenario. The maximum of the minimums or the “best of the worst”.
This approach sounds safe but it lacks maximum growth. It will definitely not result in maximum profit.
Example: This is the person who puts their money into a savings account because they could lose money at the stock market. (Colloquially in the Indian context we call them “Minimum Guarantee lovers”)
4. Opportunist (Minimax)
Opportunist decision-making is based on opportunistic loss.
They are the kind that looks back after the state of nature has occurred and said “Now that I know what happened, if I had only picked this other action instead of the one I actually did, I could have done better”.
So, to make their decision (before the event occurs), they create an opportunistic loss (or regret) table. Then they take the minimum of the maximum.
That sounds backward, but remember, this is a loss table. They want the best of the worst losses.
This approach sounds safe & smart it appeals to faster growth but it lacks sustainable growth, principles, and ethics. Also, it has a greater probability of not leading to maximum growth.
What is Decision Science?
Even though Ideally the realist(expected value)is the best decision-making approach for long-term business growth. But due to some constraints & short-term goals, other approaches also will be employed. Even sometimes a combination of these approaches could help. This is a province of Decision Science.
Decision science provides a framework of principles and decision rules, based on valid evidence, to guide leaders’ strategic choices.
A classic example of decision science in management is portfolio theory in finance, which informs decision frameworks such as return on investment, return on equity, and diversification of investments.
A striking feature of decision science frameworks is that their objective is not simply to improve the quality of decisions about financial resources but to improve the validity and consistency of the mental models that leaders use when they consider such decisions.
The key issues are not only the overall sophistication and quality of decisions but also the quality of the principles underlying those decisions.
Decision Science aims to prescribe a principled framework for Decision making based on the different types of decision-making approaches discussed above with some objectives, constraints & available information.
Decision Scientists are concerned with prescribing best methods & approaches to the management for making optimal decisions .
While most fields of research focus on producing new knowledge, decision science is uniquely concerned with making optimal choices based on available Knowledge. where optimality is often determined by considering an ideal decision-maker who is able to calculate with perfect accuracy and is in some sense fully rational.
However, pragmatically for bigger business problems, optimality is determined by a group of decision-makers & management. Ideally, optimality should be determined based on quantitative & qualitative analysis. This is where the Decision Scientist sits hip-to-hip with decision-makers and management in helping them make decisions. They are equal parts business leadership and Industry Research.
Unfortunately many think decision scientist is concerned only with quantitative research. In short Decision Science concerns every subject related to decision-making It can be both Qualitative also quantitative. To deduce the maximum number of outcomes you also need to analyze the areas that will be influential in the system.
The decision sciences concern themselves with questions like:
- “How should you set up decision criteria and design your metrics?” (All)
- “Is your chosen metric incentive-compatible?” (Economics)
- “What quality should you make this decision at and how much should you pay for perfect information?” (Decision analysis)
- “How do emotions, heuristics, and biases play into decision-making?” (Psychology)
- “How do biological factors like cortisol levels affect decision-making?” (Neuroeconomics)
- “How does changing the presentation of information influence choice behavior?” (Behavioral Economics)
- “How do you optimize your outcomes when making decisions in a group context?” (Experimental Game Theory)
- “How do you balance numerous constraints and multistage objectives in designing the decision context?” (Design)
- “Who will experience the consequences of the decision and how will various groups perceive that experience?” (UX Research)
- “Is the decision objective ethical?” (Philosophy)
It also includes decision analysis, risk analysis, cost-benefit, and cost-effectiveness analysis, constrained optimization, simulation modeling, and behavioral decision theory, as well as knowledge from parts of operations research, microeconomics, statistical inference, management control, cognitive and social psychology, and computer science.
How does Decision Science Enhance Decision Making & Industry Research?
1. Data-Driven vs Data Inspired in Decision Making
Good decision-makers commit themselves to their default decision upfront. They will choose it by making a judgment call about which action is the lesser evil. To deduce the lesser evil action, decision-makers need to imagine all possible outcomes. To imagine the maximum number of possible pragmatic outcomes. They need to be data-driven not data-inspired.
One of the Key Job of Good Decision maker is to imagine maximum number of possible states of the world.
Many decision-makers think they’re being data-driven when they look at a number, form an opinion, and execute their decision. Unfortunately, such a decision will be “data-inspired” at best. Data-inspired decision-making is where we swim around in some numbers, eventually reach an emotional tipping point, and then decide. There were numbers near that decision somewhere, but those numbers didn’t drive the decision. The decision came from somewhere else entirely. It was there all along in the unconscious biases of the decision-maker.
Regarding Long-term goals & strategy, It attempts to capture the universe of possible decisions & outcomes, then evaluates the consequences of each decision-outcome combination. Decision-makers need decision scientist’s expertise to do the above things . The decision/action with the best-estimated consequences, given potential outcomes & objectives, is then chosen by the Decision maker.
Typical Example of considering each decision-outcome combination.
2. More Actionable Insights for Business Growth
To capture possible decisions and their outcomes. And for evaluating the consequences of each decision-outcome combination for better decision-making. It needs certain analysis & research. This is one of the focus areas of Industry Research
Industry Research without decision science is Academic Research
While Academic research focuses on producing new knowledge, it helps us to make sense of information, to learn from it. It is done for the purpose of advancing scientific understanding.
But in Industry Research, we must make a decision & drive action from the knowledge produced. This is the province of Decision science.
The problem often found in Industry Research is, Many Researchers have a natural tendency to work with the lens of academic research or their specialization. And on the other side management tends to expect only actionable insights many times it does not hesitate to exclude the greatest insights if it is not actionable or profitable.
We could not blame them both, This is where decision science becomes handy it bridges the gap between Industry Researchers and Decision Makers by seeking to make plain the scientific issues and value the consequences of each decision-outcome combination and identify tradeoffs that might accompany any particular action, or inaction.
I am not saying Industry Researcher is not capable of bringing actionable Insights. In fact, they already practice decision science to some extent when they try to be good industry researchers.
But many times research cannot be done only with the lens of industry research it also needs a lens & spirit of academic research for quality results. Again planning, conducting, and executing Industry research is not an easy job at all. Many good Industry researchers are capable of doing this but in this age of data, it is too much load for them. It is more or less equal to asking UX Designers to conduct UX Research. Yes, both can be done by the same person. But If it is done by different people, the output value is high.
3. Better Solutions need multidisciplinary efforts.
One of the common mistakes many companies do when they integrate a new domain is they expect everything from a single person or specialization. They set the bar very very high with multiple responsibilities for a single job role later they realize that the bar is not a practical one. After realizing the reality of the job market they delegate the job responsibilities by bringing another new role or specialization. A good example would be Data scientists in present and UX Designers in past.
In the case of data scientists, they need to collect data, they need to build ML models, analyze data and bring insights. Also, importantly bringing actionable insights with recommendations for decision making. Sounds simple but it's not that easy. It's truly heavily loaded especially if the organization has more data. Also, Data Scientists do not necessarily need to be experts in social science or business. Over-emphasized quantitative view or single-dimensional view might lead to biased insight.
This is where Decision Scientist sits hip-to-hip with Industry researchers in helping bring Actionable Insights for Business. As I said earlier they are equal parts of business leadership and Industry Research. Simply they are generalists in Industry research.
“Decision Scientist turn business problems into Research Questions, Research findings into Business Insights”
For Decision Scientists, the business problem comes first. The analysis follows and is dependent on the question or business decision that needs to be made. The Decision Scientist is looking to find insights as they relate to the decision at hand.
Example decisions might include Age groups to focus on, the most optimal way to spend a yearly budget, or figuring out a way to measure a non-traditional KPI.
By bringing the decision scientists, you automatically reduce a certain amount of burden on Industry researchers with regards to a very deeper understanding of business & strategy. You can onboard the best Behavioural Scientist who necessarily does not need to be a person with a greater understanding of your particular business domain knowledge. Decision Scientists will take care of that by collaborating with them.
Decision Science also automatically provides a space for the convenient recruiting and management of Industry researchers. If you ignore decision science, then you would be searching for a small population that would qualify your high expectation bar or you might end up with average recommendations with less actionable insights or average quality of research. This would be a very relevant problem statement in the upcoming years.
It may also create a space for more junior researchers & analysts recruitment. Because the major reason the junior researchers are not considered for the Industry researcher role is their credibility in bringing actionable insights & deeper understanding of business.
Decision Science is the road with multiple lanes built by decision scientists, where decision-makers will drive the “car” called the “company” in the lane they feel safe. Without decision science roads, they might not need to travel the car via off-road and encounter problems. But they might need to travel on a self-built road that has a greater probability of being less durable & less efficient especially if that car is large.
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References & Sources:
- Measuring decision-analytical competence: a psychometric online performance test (https://www.researchgate.net/publication/317843515_Measuring_decision-analytical_competence_a_psychometric_online_performance_test)
- Decision Theory (https://people.richland.edu/james/summer02/m160/decision.html)
- Pessimist, realist, optimist stock illustration (https://www.istockphoto.com/vector/essimist-realist-optimist-gm508855722-85474035)
- The First Thing Great Decision Makers Do (https://hbr.org/2019/06/the-first-thing-great-decision-makers-do)
- The study of prescriptive and descriptive models of decision-making (https://thesai.org/Downloads/IJARAI/Volume1No1/Paper12-The_Study_Of_Prescriptive_And_Descriptive_Models_Of_Decision_Making.pdf)
- Bridging the gap between science and decision-making (https://www.pnas.org/content/110/Supplement_3/14055)
- Data Science vs Decision Science (https://towardsdatascience.com/data-science-vs-decision-science-8f8d53ce25da)
