How to make good decisions?
Decision -Uncertainty, Risks and Bias
The tactics for making good decisions each time in our investments journey remains an unsolved mystery. Good decision makings require thoughtful analysis of the problems and evaluating future probable outcomes. One of the most important things one should remember while taking critical decisions is ‘You can be roughly right rather than being precisely wrong’. Investment field is a probabilistic science, where there is a huge payoff in taking right decisions. Since we are operating in a probabilistic field, it would we wise to focus on our decision-making process than on outcomes.
Why Making Good Decision Is Hard?
In the decision-making process, we are indirectly competing with future uncertainties and risks. It would be very difficult to predict all the future states and assign probabilities precisely. Even though we cannot completely eliminate uncertainty and risks, we can minimize them. One of the main reasons we are not able to make rational decisions is because we are an emotional creature, our emotions and biases hinder us from making rational decisions.
Instead of asking ‘How to make good decisions’, we shall invert our problem and state ‘How not to make bad decisions’. Even though both the questions are same, the perspective by which we analyze any given problems, by keeping both the questions in our hand is completely different. In this post, we will uncover the mystery of ‘how not to make bad decisions ‘.
How not to make bad decisions?
The most common myth about decision making is that uncertainty and risk are same. We must be able to classify our decisions clearly whether it is risky or uncertain. Classifying our decision environment is the first step in not making bad decisions.
Let us consider a simple decision-model for our better understanding
How to read the table?
Probability has two categories.
Known – this is the situation in which we can estimate certain probabilities of future outcomes.
Unknown – this is the situation in which we cannot estimate certain probabilities of future outcomes
States has two categories.
Known – this is the situation in which we can estimate states of the future outcomes.
Unknown – this is the situation in which we cannot estimate states of the future outcomes.
There are four combinations.
Known known- we can estimate both probabilities and outcomes.
Known unknown- we can estimate probabilities but not outcomes.
Unknown known- we cannot estimate probabilities but we can estimate outcomes.
Unknown unknown- we cannot estimate both probabilities and outcomes.
Risks come under the category of ‘known known’, which means we can estimate both probabilities and the states of the outcome. For example, let us consider a finance company which lends money to the retail people. After analyzing 100 documents they have shortlisted 20 candidates for giving a loan. The most important question the bank should think is, ‘what if the candidate receiving the loan defaults in future?’.
The situation the finance company facing now is not considered to be ‘uncertain’ situation it should be considered as ‘risky’ situation because both the probabilities and states of the outcome is known.
From our example, the possible states:
The candidates will pay interests regularly.
They will default in future.
On assigning probabilities to the states:
The probability by which the candidate will pay interests regularly can be computed by looking into their income documents, how much assets he owns and how much remaining loan amount pending to be paid. Even though they cannot be precisely calculated but they can be estimated.
The probability of default can be calculated by looking his past records of defaults and his monthly recurring income records etc.
Since both states and probabilities can be estimated we consider it to be a risky situation. Now we understand why the banking system is a ‘risky industry’ and not ‘uncertain industry’.
Uncertainty comes under the category of ‘unknown known’, in which the state can be predicted but their probabilities cannot be estimated.
For example, we take life insurance against any uncertain events happening in life, such as accidents or some sudden death. Even though we know the possible states (accidents, sudden deaths etc) but we cannot assign the probability to those outcomes (calculating probabilities for car accidents and sudden death is not possible because they are extreme events and they occur rarely). So, we take insurance against the uncertainty.
Now we can understand that insurance is an ‘uncertain industry’ and not ‘risky industry’. In short, risks can be estimated and controlled to some extent but uncertainty cannot be estimated and controlled.
Emotions and Biases
Emotion and biases play a large part in making decisions. While taking decisions we tend to operate with rules of thumb(heuristics), which are generally correct and saves lots of time but heuristics are associated with biases that can lead to deviate from making right decisions. Heuristics includes availability bias (rely on information that is available rather than relevant information needed), anchoring bias (placing too much weight on an anchor figure) and the list goes on. Great decision makers(investors) are those who not only understand these concepts but take steps to manage or mitigate behavioral biases in their investment process.
Most Important biases and Mitigation
There are commonly three important biases that we will be uncovering.
- Overconfidence bias.
- Confirmation bias.
- Recency bias.
We generally tend to overestimate our own capabilities in making decisions. We tend to give more importance to the outcome we prefer to occur, rather than creating a list of most probable outcomes. Investors having overconfidence bias get trapped into the state known as ‘illusion of Control’. An illusion of control makes the people behave as if they might have some control over the situation, when in fact they have none. To overcome this bias, one should be very conservative and open-minded while taking decisions. The solutions seem to be simple but it’s not easy.
Confirmation bias is the tendency to intercept or pick information that confirms our existing beliefs. In a certain situation, we would have already made certain decisions in our mind and now we would be only seeking the information that confirms that the decisions we have made and we ignore rest of the data. This bias could be overcome, just by doing the exact opposite of what we would do in confirmation bias. That is while making certain decisions we should find all the data that could disconfirm our decision.
There is the tendency of overestimating of things that happened lately, this is the reason we get over excited for some time after some things happen to us and get very depressed if some things don’t work out for us. This nature of investors is very harmful in making decisions since we will not be able to take a rational decision. If we have made few good decisions, we tend to extrapolate and think that we will make only good decisions and if all our previous decisions ended in disasters, we would naturally think that we are not capable of taking good decisions at all. In order to overcome this bias, we must treat each decision making an independent process, which means that, past success don’t guarantee future success and past failures don’t indicate future failures.
Decision making is a process of fighting one’s own biases
Decision making is a mental process and one can improve the decision-making process only by constant practice and not by mere reading about decision making.
One has to fight his own bias in order to overcome irrationality and make rational decisions. The journey is tough but rewarding at the end is priceless.
Decision-making process is like a long journey in uncovering the mystery puzzle, which requires a mental framework of visualizing the problem in the multi-disciplinary approach. In this post, we have just covered the prerequisite necessary for our journey. In our coming post, we will discuss many ‘Mental Models’ necessary to survive in this long journey and make good decisions.