Fundamentals of Risk in Decisions – Part 1

When presented with a choice between two solutions, with an obvious difference in cost and value, you should always choose the cheaper one, right? We all know this is not the case, as it’s just not that simple. In fact, many times the more expensive choice may be the right one when all factors are considered. But it is important that the cost premium delivers a value that exceeds the cost differential and potential for failure. This is a tangible component to the decision process.

The intangible factors that influence decisions are what we can generalize as “risk”. First, there is risk in every decision, although not to the same degree, whereas there is not always a “return” or “reward” associated with a risk. There is risk in making a decision, there is also equal risk in not making a decision. The magnitude of the risk is determined by tangible and intangible factors.

The major components of risk are: risk exposure, risk tolerance, confidence and trust, probability and chance, and the size of the risk or decision. Counterbalancing risk is the notion of return, which is comprised of the same factors, but refers to the ability to achieve value goals. A robust risk analysis methodology establishes a framework for identifying, measuring, evaluating and objectively comparing these factors.

The Three Rules of Risk Analysis

A fundamental risk analysis concept is that our response to risk is a heavily emotional reaction that influences our decision and even our ability to make a decision. In deciding between two options, the decision that will likely deliver the most favorable outcome will adhere to these three general rules:

  1. The ratio of the investment to the expected return influences the decision between financial risk and performance risk
  2. The level of risk tolerance should exceed the level of risk exposure
  3. The upside value potential should exceed the value being put at risk

Uncertainty Causes Inertia

In the first rule, the emotional connection to financial and performance risk is evaluated. First, we have the consideration of how the decision will impact us or our organization if the decision turns out to be a bad one.

  • What if this doesn’t turn out as expected?
  • What if it ends up costing more and taking longer to implement?
  • Am I getting locked into something I’ll have trouble getting out of?

As the uncertainty of these types of concerns increase, the likelihood of a decision stalling will increase as well, because it may appear that doing nothing is less risky. But making no decision carries risk exposure as well, in terms of lost opportunities and unmitigated risk exposure. This type of risk can be categorized as performance risk, as it is associated with the success and probability of failure in the competing solutions.

More Money, More Emotions

Second, the size of the decision’s cost and the potential revenue or value being put at risk also makes the decision more of an emotional one. This type of risk can be categorized as financial risk, being associated with the ratio of the investment to the outcome. For example, the game of poker is basically the same whether you are playing a friendly game for pennies or playing for $1,000 chips in Las Vegas. But you play the game very differently when the stakes of losing are significantly higher, and consequently, you are less willing to embrace risk.

If you were presented with an opportunity to make a sizeable return on an investment, but the amount you needed to invest was large, then you might not be inclined to accept the opportunity without a lot of consideration. But if the same situation was presented and you only had to make a very small investment, then you might accept the opportunity immediately.

For example, let’s look at a lottery where the jackpot is $100M but a ticket costs $10M. For simplicity, let’s say only 10 tickets are sold, meaning the odds would be 10:1 against you. Even if you improve your chance of success by purchasing 9 of the ten tickets, you’re still putting $90M at risk to return $100M which is an unacceptable risk unless you are extremely lucky or reckless. In this scenario, the financial risk has become so large that all other considerations are irrelevant.

Now, let’s say the ticket only costs $10 and there’s still only 10 tickets to be sold (not in reality, but we’re making a point here). Even if you could buy 9 of the 10 tickets, it would still be a highly acceptable risk because the potential for return vastly exceeds the dollars being put at risk. And you would probably make a buy decision without having to spend any time analyzing the downside, since you’re risking $90 to make $100M. What has happened in this scenario is that financial risk has become insignificant for all practical purposes. Granted, these are extreme examples, but they do illustrate the point.

Shrink Your Decisions Down to Size

In short, the amount of money being put at risk can emotionally overshadow the logical value presented by a solution. It’s a natural and healthy fear about increasing risk that introduces decision bias, and these biases tend to generate indecision as an outcome. If we can reduce the relative size of the decision in relationship to economic and technical alternatives, and quantify the fear-factors, then decision logic can begin to override the resistance (fight or flight reaction) or the “do nothing” (deer in the headlights) reaction. For more discussion about biases and blind spots, see our Cloud Economics blog.

In the next installment of this blog series, we’ll take a look at rule number 2. This rule defines the levels of risk exposure and tolerance and describes how the degree of risk tolerance needs to be greater than the level of acceptable risk exposure.

Resources to learn more:

About the Authors

Craig Stanley

Group Product Line Marketing Manager / Cloud Economics at VMware

Craig Stanley is a Group Product Line Marketing Manager in the Cloud Economics group. He specializes in economic cost analysis and the art of decision making. Craig has been in the IT industry for over 35 years, including 9 years as a Gartner VP and Research Director and 8 years at VMware. As a Gartner analyst, he specialized in datacenter benchmarking, trends and analysis and authored several research notes. At VMware, Craig has been responsible for developing methodologies that help our customers understand how to effectively connect and align their IT initiatives to their desired business objectives. As a Cloud Economist, he drives conversations around the economic value and benefits of VMware’s Cloud offerings. Craig holds a BS degree from the University of South Alabama and an MBA from The Citadel in addition to a US Patent for a mathematical benchmarking methodology. He is based in the US.

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