Risk intelligence

The term risk intelligence (often also risk competence in German-language literature ) has its origins in education and psychology. However, it can also be transferred to the fields of law, linguistics, biology and, in particular, the economic context.

While the risk assessment undertakes the analysis and evaluation of the risk, the risk intelligence describes the design of risky decisions and can therefore be assigned to behavioral economics .

definition

There are various approaches to defining the term risk intelligence.

According to Apgar, risk intelligence is composed of all past and future experiences that are relevant in solving problems that require an understanding of risks. He also emphasizes that the term can refer to both an individual and an organization's ability to effectively weigh risks. This includes the categorization, characterization and assessment of dangers, the perception of relationships and acting on the corresponding information. Effective communication and adaptation to new circumstances are also crucial.

Funston and Wagner choose a dynamic approach. Above all, they aim at the added value that risk-intelligent action can bring about in improved decision-making by controlling risks. Risk intelligence is the crucial ability to differentiate between two types of risk. On the one hand, the risk is meant, which should be minimized to prevent damage and, on the other hand, the risk to be absorbed, which is necessary to achieve competitive advantages.

Evans, on the other hand, cites two points of criticism of the two definitions of terms mentioned by Funston and Wagner or Apgar. The definitions used are too vague for him to understand and include too many skills that they cannot be implemented in practice and therefore cannot be scientifically measured. For this reason Evans limits himself to the definition that risk intelligence is the cognitive ability of an individual to accurately predict probabilities. However, this definition does not include objective probabilities, but rather presupposes the subjective interpretation of the probabilities. Thus, Evans involves assessing the level of knowledge a person has on a particular subject. Furthermore, the author emphasizes that he deliberately dispenses with value judgments in his definition by only referring to probabilities, regardless of whether they are aimed at a negative or positive result.

In their definition, Chrobok and Gleißner focus primarily on the risk intelligence of companies. This means the competence of an organization that creates transparency about the individual risks and the aggregated overall risk scope, and thus adequately takes the risks into account in its business decisions. As a result, a risk-intelligent company must be able to manage the risks efficiently.

In contrast, Drewniok uses an individualized view of the concept of risk intelligence. For them, it represents the ability to place oneself in the middle of underestimating and overestimating oneself. As a result, similar to Evans, the focus is on the assessment of the certainty of knowing about an issue. However, due to the different risk attitudes of people, this also means that the individuals have different starting positions in order to improve their risk intelligence. However, the skills required are identical.

While the definitions mentioned so far look at risk intelligence from the business side, Craparo, Magnano, Paolillo and Costantino focus on the psychological explanation of the term. According to the authors, risk intelligence is a psychological resource that contains both cognitive and emotional properties. Specifically, it is the ability to effectively determine the pros and cons of a decision and enable a person to perceive unsafe situations as an opportunity rather than a threat.

In summary, the statement can be made that the definitions differ in the addressee, i.e. the organization or the individual, and in the form of a value judgment.

meaning

While companies deal specifically with risk management and strive to avoid negatively associated risks, dealing with risk intelligence, i.e. the targeted use of risky decisions, receives less attention. Economic catastrophes such as the global financial crisis from 2007 onwards prove that the importance of risk-intelligent actions is often underestimated.

Both economic and political uncertainties are increasingly shaping the markets and therefore require the conscious handling of risk intelligence and thus the ability to objectively assess risks and to be able to correctly estimate probabilities. An individual with high risk intelligence is better able to adequately interpret an uncertain information structure or change processes.

Drewniok describes taking risks as a prerequisite for long-term growth, success and the competitiveness of a company. The American financial analyst Tilman also explains the use of risk intelligence for investors and financial institutions as a key component for long-term success. In a study by McKinsey, Lovallo and Sibony also demonstrated a six percent increase in success through the use of risk-intelligent, trained decision-making processes by managers in the company. Similarly, Festag recognizes a general need for support to improve risk intelligence, especially for specialist staff such as business administrators, whose risk-intelligent decisions influence economic and social developments. Here he goes into the innate predisposition to risk-intelligent behavior in every person. The prerequisite for the individual development and further development of these skills and thus for an appropriate handling of risk is an early imparting of risk competence through educational measures. This particularly refers to the time from kindergarten and before starting work.

Measurement

Between 2007 and 2012, researchers at the Max Planck Institute for Human Development in Berlin and the Michigan Technological University developed a psychological test for risk intelligence. With just a few test items from the area of ​​percentage calculation, the "Berlin Numeracy Test" is intended to determine the ability to interpret information on risk probabilities in the shortest possible time. The results of the research showed that risk intelligence is closely linked to a person's mathematical understanding or statistical competence with regard to the interpretation of data.

Meanwhile, David Apgar created the Risk Intelligence Quotient (Risk IQ), a procedure designed to measure the test subjects' ability to evaluate learnable risks. The scoring process for determining the Risk IQ is based on five elements that relate to different types of risk. In relation to people who are confronted with similar risks at work or in everyday life, a self-assessment should give a value of 0-2 for each of the elements. While 0 means that other people in the same occupational group are better able to assess the risks, a value of 2 stands for an above-average ability to judge learnable risks. In addition to measuring risk intelligence, the test should also provide the basis for improving below-average elements.

Chrobok and Gleißner consider risk intelligence to be part of business risk management. In 2012, the two authors drafted a list of 14 quantitative orientation questions in a specialist article, which should help the management of a company to determine the "Risk Intelligence Indicator" (RII) by means of self-assessment. A total that corresponds to the RII can be created using three different answer options for each sub-question. This indicator is intended to offer the possibility of assessing the existing risk intelligence in the company and at the same time to indicate improvement approaches.

Craparo et al. in a psychological study developed a scale to measure “Subjective Risk Intelligence” on the basis of the four factors imagination, problem-solving ability, attitude towards uncertainties and emotional sensitivity to stress. 21 statements about certain behaviors and psychological states are to be evaluated with regard to one's own life experience. The individual risk intelligence is determined on the basis of the characteristics of the answers given.

Improving risk intelligence

Most people have low risk intelligence. However, this is necessary in order to cope with the increasing unpredictability and uncertainty. For this reason, the ability to act in a risk-intelligent manner should be methodologically improved. In this context, Evans names general requirements that are to be placed on methods for promoting the development of risk intelligence. On the one hand, the methods are intended to accustom the users to defining the probabilities in numerical values. This is crucial in order to also indicate the risks in percentages. This makes the risks explicit. Statements like “it is likely” are worded too vaguely for people to interpret them differently. On the other hand, the instrument used should provide quick and clearly defined feedback. This can be achieved, for example, by comparing the forecast with the actual value. In addition, the method should only cover a narrow specialist area. It is also important to gain experience with probability statements. This allows individuals to train their risk intelligence.  

Another approach to expanding risk intelligence consists of three points. First, it is necessary to reflect on your own risk attitude. The risk intelligence must not be confused with the risk attitude. While the former is a cognitive ability, risk attitudes are traits that make some people risk averse or risk-averse. Second, it is important to understand the psychological side of the risk. There are various distortions of perception that impair risk perception including the analysis and assessment of the risk.  

• Overconfidence : This is the discrepancy between what you know and what you think you know. It is therefore often the cause of risky decisions.
• Optimism Bias: People are generally too optimistic about the future and thus overestimate the probabilities of positive developments. This makes it possible for too many risks to be taken.
• Loss Aversion: This refers to the tendency to weight losses higher than equivalent gains. This means that risks are avoided despite a good starting situation.
• Confirmation Bias : When making decisions, people prefer information that confirms their goals and thereby neglect information that disproves their beliefs.
• Hindsight bias: This has the consequence that information is judged differently in retrospect based on knowledge of the result. In doing so, it restricts the development of risk intelligence, since one cannot learn from mistakes.

As a third point, it is stated that basic statistical knowledge is required, since the indication of percentages, estimated values ​​and value ranges indicate a high level of risk intelligence rather than numerical precision. Calibration as a method to improve risk intelligence should also be considered. In doing so, probabilities of a situation, the result of which will already be known or will be known in the near future, are estimated and compared with the actual result. Consequently, a perfect calibration is aimed for by matching these two values ​​in all runs.

By dividing the definition of risk intelligence into an individual-related and organizational perspective, measures to improve risk intelligence are shown for both categories. At the organizational level, it is essential to change the error culture in companies. In order to achieve risk intelligence, it is necessary that constructive feedback is given. The risk dialogue continues to gain importance in this context. By sharing information about the risks with others, the influence of confirmation bias can be reduced, for example . In addition to the measures mentioned above, Drewniok is convinced that risk intelligence can be further developed individually through a large amount of generalist knowledge. The formation of heuristics can also support this process.

Research and application

Risk intelligence is relevant in various disciplines in research and practice. The Harding Center for Risk Literacy at the Max Planck Institute for Human Development, founded in 2009, examines the behavior of people in risk situations with the aim of improving the risk literacy of the population. The research focuses, among other things, on topics from the health sciences. Gigerenzer et al. Problems that medical professionals, patients and journalists have when interpreting health statistics. In the social sciences and behavioral research, too, risk intelligence is investigated in adolescents and drug users, among others. According to Evans, some groups of people, such as meteorologists, usually have above-average risk intelligence. However, this competence is also important for other professional groups, for example doctors or financial analysts. However, risk intelligence is not related to the general intelligence quotient.

In business administration, risk intelligence is a main topic in the context of finance. Particularly in controlling , whose task it is to prepare company data that is used for future decision-making, a high level of risk intelligence is required. The competence of a company and its employees to identify individual risks, to summarize them to form an overall risk and to include them in corporate decision-making, can in this context be referred to as risk intelligence. A high level of risk intelligence in the sense of recognizing operational risks and the associated avoidance or limitation of losses is important for financial institutions and insurance companies. For this, it is necessary to include information from risk management in the company's strategic decision-making.

However, the topic is also increasingly being used in other areas of business administration. Apgar developed a framework for companies outside the financial sector in which he transferred methods from financial risk management to general risk management systems. Florea et al. have emphasized in their work the importance of implementing risk competence in brand management. The authors developed a model that serves as a management guideline for the inclusion of brand management risks in the company's risk management.

Furthermore, Chrobok and Gleißner compare the concept of risk intelligence with the Control and Transparency Act (KonTraG and IDW PS 340). Accordingly, this law primarily serves to create transparency about risks. However, risk intelligence also focuses on reactive or preventive measures to circumvent or reduce risks. This concept is therefore not limited to the quality of the risk management system, but also focuses on the ability of an organization to cope with future uncertain developments.

criticism

The different definitions of risk intelligence in the literature illustrate the complexity of describing the term appropriately. This often offers approaches to criticize the construct of risk intelligence. First of all, it is important to clearly distinguish the decision-making situations under security, risk and uncertainty. Risk intelligence deals with decisions under uncertainty, which means that some information (for example the probability of occurrence) is unknown or has to be estimated using small samples. This procedure creates a subjective component in the assessment of the options for action. This subjective assessment is the main argument for the criticism of the construct of risk intelligence. The lack of an operational construct makes measuring risk intelligence very complex. Craparo, Magnano, Paolillo and Costantino attempt to carry out a suitable measurement using the Subjective Risk Intelligence (SRI) parameter; however, the SRI they define is also related to other individual and psychological factors that the researchers do not take into account for reasons of better verifiability become. When measuring risk intelligence using the RII (Risk Intelligence Indicator) according to Chrobok and Gleißner, a list of 14 quantitative orientation questions is used that are intended to evaluate the risk intelligence of a company's management. These questions are then answered based on a self-assessment, which can also falsify the results. The complexity of the objective measurement of risk intelligence becomes clear here too.

In addition to the lack of an operational construct and the associated difficulties in measurement, Esser criticizes risk intelligence, especially from the neuro-economic point of view. Systematic errors often occur when assessing risk intelligence. The WYSIATI rule (What-You-See-Is-All-There-Is) according to Kahneman describes a limitation in the brain. Only that is consciously perceived, which also flows into the decision. As an example, Esser chooses two questions for drivers. First, a car driver is asked whether he or she would consider himself a good driver and then the driver is asked whether he is an above-average driver. The first question is usually answered very quickly with "Yes". The answer to the second question is much more complex and in order to give the correct answer, the respondent would have to know the measurement method and the resulting average. But since this is very difficult and the classification is not possible in the short time, the easy question is avoided and the second question is also answered with "Yes". Thus, when answering the question, only what the respective person knows is taken into account (“I am a good driver”). The example of drivers can easily be transferred to other groups of people (e.g. fund managers or doctors) and thus clearly shows that people are subconsciously deceived by their brains when making decisions. 

In connection with the WYSIATI rule and the dominance of fast thinking, another systematic error occurs, namely the availability heuristic. The problem here is that the probabilities for the different events are estimated according to the information available. For the brain, weighing and calculating risks is a strenuous process, whereby decisions are made through intuition. The basis of intuition, however, is mainly information that is currently available or easily accessible. As an example, Esser cites a study by Gerd Gigerenzer, who examined the disproportionate trend in road deaths in New York after September 11, 2001. Many citizens thought flying was too dangerous due to the terrorist attacks and switched to their cars. The likelihood of a plane crash hadn't changed, however.

Individual evidence

1. ↑ ^{a b} Sebastian Festag, Uli Barth: Risk Competence, Assessment of Risks. Assessment of risks . In: Federal Office for Civil Protection and Disaster Aid (ed.): Writings of the Protection Commission . No. 7 . Bonn 2015.
2. ↑ ^{a b c d e f} Babette Drewniok: Risk competence is a core skill in an uncertain environment. How can you improve it? In: CONTROLLER magazine . No. 5 , 2014, p. 32-38 .
3. ^ ^{A b c} David Apgar: Risk intelligence. Learning to manage what we don't know . Ed .: Harvard Business School Press. Boston 2006.
4. ↑ Frederick Funston, Stephan Wagner: Surviving and thriving in uncertainty. Creating the risk intelligent enterprise . Ed .: John Wiley & Sons. Hoboken 2015.
5. ^ ^{A b c} Dylan Evans: Risk Intelligence . In: Sabine Roeser, Rafaela Hillerbrand, Per Sandin and Martin Peterson (eds.): Handbook of Risk Theory. Epistemology, Decision Theory, Ethics, and Social Implications of Risk . Springer Netherlands, Dordrecht 2012, p. 603-620 .
6. ↑ ^{a b c d e} Stephan Chrobok, Werner Gleißner: Risk Intelligence - Indicator for the future orientation of controlling . In: CONTROLLER magazine . No. 5 , 2012, p. 70-71 .
7. ↑ ^{a b c d e} Giuseppe Craparo, Paola Magnano, Anna Paolillo, Valentina Costantino: The Subjective Risk Intelligence Scale. The Development of a New Scale to Measure a New Construct . In: Current Psychology . 2017.
8. ↑ ^{a b c} Axel Esser: Making intelligent risk decisions. In: risknet.de. March 17, 2016, accessed July 17, 2018 . 
9. ↑ Matthias Fischer, Markus Hinterberger, Andreas Höss: The biggest bubble of all time - cheap money created debt bubbles and inflated asset prices. Because interest rates are rising again, the fear of a crash is growing. In: Euro . No. 4 , 2018, p. 38-43 .
10. ^ Leo Tilman: Risk Intelligence: A Bedrock of Dynamism and Lasting Value Creation. In: The European Financial Review. December 28, 2013. Retrieved July 17, 2018 . 
11. ↑ Dan Lovallo, Olivier Sibony: The case for behavioral strategy. In: McKinsey Quarterly. March 2010, accessed on July 22, 2018 . 
12. ↑ Edward T. Cokely: Decision-making has to be learned - new test measures risk intelligence. Max Planck Society, April 10, 2012, accessed on July 17, 2018 . 
13. ↑ Dylan Evans, Kristina Enderle da Silva: Risk intelligence is essential . In: PERSONALmagazin . November 2013.
14. ↑ Gerd Gigerenzer, Wolfgang Gaissmaier, Elke Kurz-Milcke, Lisa Schwartz, Steven Woloshin: Helping Doctors and Patients Make Sense of Health Statistics . In: Psychological Science in the Public Interest . No. 8 , 2007, p. 53-96 .
15. ^ Dorian-Laurentiu Florea, Claudiu-Catalin Munteanu, Alexandra-Elena Postoaca: Integrating risk literacy into brand management . In: Review of International Business and Strategy . No. 26 , 2016, p. 204-218 .
16. ^ Daniel Kahneman: Thinking, fast and slow . Penguin Books, London 2012.
17. ↑ Gerd Gigerenzer: Risk: how to make the right decisions . 5th edition. Bertelsmann, Munich 2013.