**impact likelihood = probability of risk * impact likelihood given risk has occurred**

Prediction markets are exceptionally well-suited for doing these kinds of impact assessments, especially in public policy concerns where risk probabilities are not easily derived from historical data or where public participation is an essential part of the deliberation process. You can take an impact assessment one step further by measuring the magnitude of a given potential impact with regard to its likelihood, commonly referred to as risk exposure. Risk exposure is the risk adjusted value of the consequences should a risk become realized.

Once you've derived a likelihood of an impact occurring, you can then quantify your risk exposure by multiplying the probability of the event occurring by the value of the total loss of risk.

**risk exposure = probability of risk * total loss of risk**

... or to calculate the risk exposure of an impact:

**risk exposure = (probability of risk * probability of impact) * total loss of risk**

The total loss of risk can be thought of in terms of the value of an asset, shift or loss in demand, number of people affected, value of ecosystem services, or any other quantifiable amount of utility that may be lost if a risk is realized. Total loss of risk may be something you already know, such as the value of your real estate assets or it could be a value derived from a third prediction market. For example, asking a question in a prediction market such as "how many barrels of oil

*could*flow through phase IV of the Keystone pipeline in 2014?" will give you the total loss of risk of barrels of oil coming in from Canada via the Keystone pipeline. Multiplying that number by the probability that the segment is not built because of environmental policy outcomes will give you your risk exposure which can be weighed vs your investment.As a risk manager, an important thing to remember when dealing with total loss of risk exposures is that a low probability of a high loss of risk may be equivalent to a high probability of a low loss of risk. Therefore, many risk managers will construct risk matrices when evaluating their portfolio's risk exposure in order to see a full range of their risk decisions. Loss of life, for example should not be boiled down to risk exposure as a low probability of many lives lost is not equivalent to a high probability of a few lives lost. They are just not comparable.

As you can see, building a resilient portfolio is dependent upon minimizing your risk exposure. This can be done by: 1) setting aside enough resources to cover your risk exposure; 2) reducing the amount of total loss of risk you are taking on; 3) reducing your risk probability; or 4) mitigating the potential impacts should a risk occur. Finding the right balance between these 4 variables is the art and science of risk management.

We feel that prediction markets naturally lend themselves towards robust risk assessment tools. If you have used Inkling for risk management purposes in your organization please write us, we'd love to hear about it.

Pat Carolan

Inkling Markets

We feel that prediction markets naturally lend themselves towards robust risk assessment tools. If you have used Inkling for risk management purposes in your organization please write us, we'd love to hear about it.

Pat Carolan

Inkling Markets

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