Fundamentals of Risk Management

1. Fundamentals of Risk Management

2. The Ubiquity of Risks in Today’s Socio-technical Systems • Automobile and plane crashes • Toxic chemical spills and explosions • Nuclear accidents • Water and food security (contamination, shortage) • Genetic manipulation • Infectious diseases (AIDS, bird flu) • Global climatic change • Ozone depletion • Extinction of species • Oil dependence • War and terrorism • On and on and on………

3. The Rational Actor Standard • Choice of actions • Range of possible outcomes • Assign probabilities to outcomes • Order outcomes according to preferences • Select optimal action • Expected value calculations

4. The Rational Actor “acts” • Action is “…. purposeful behavior. Or we may say, action is will put into operation and transformed into an agency, is aiming at ends and goals, is the ego’s meaningful response to stimuli and to the conditions of its environment, is a person’s conscious adjustment to the state of the universe that determines his life.” Ludwig Von Mises, Human Action (1949) : p. 11

5. Human action vs. Human behavior • Action has reasons, behavior has causes • Action: “institution,” “will,” “duty,” “responsibility”, “obligation”, “right”, “constitution”, “contract”, “custom”, “traditions,” and “collective choices.” • Behavior: “social organization,” “behavioral measurement,” “human relations,” “reliability,” “validity,” “organizational design,” “attitude,” “human performance,” “power,” “performance evaluation,” and “leadership.”

6. Risk Evaluation vs. Management: A Proposed Distinction • Risk Evaluation: the cognitive process of determining or specifying a risk prior to making a behavioral commitment • Risk Management: the cognitive and behavioral process of designing and implementing controls on risky systems

7. Evolution of Risk Management • Early humans: risks small scale, local; management relatively straightforward • Technology led to evolution of systems with larger scale, greater complexity • Risk evaluation and management, today define the key analytical lens for attempting to anticipate the consequences of human action in social and environmental systems

8. Definition of a System • “A section of the universe which an observer chooses to separate in thought from the rest of the universe for the purpose of considering and discussing the various changes which may occur within it under various conditions” (J. W. Gibbs) • Usual distinction made between (1) an observed object, (2) a perception of the object, and (3) a model of the object

9. A Generalized Structural Graphic of a System Environmental boundary Inputs Outputi Process Inputt Outputj Inputu Feedback Environment

10. The Concept of a Structure • A generic notion: the “structure” of a building, a brain, an organization, a society, a research design, a decision process, a “system” • A discernable (persistent) pattern or set of relations that connect a set of terms, nodes, or objects • Formal bodies of scientific knowledge have a “structure”

11. Generic Structure of a Body of Scientific Knowledge: (Modified from Warfield, 1990) Hypothesis Methodology Theory Assumptions Applications denotes a “steering” relationship

12. Systems concepts provide a means of integrating basic tools of thinking • Memory • Association • Pattern discernment and recognition • Reason • Experience • Invention • Experimentation • Intuition

13. Risk Management through Systems “Control” • Choosing the inputs to a system so as to make the state or outputs change in (or close to) some desired way. (Arbib) • A constraining effect on a variable • A directing influence on the behavior of a system, or the setting of the parameters of a system. • Any one-way communication, which by definition conditions a receiver's behavior in some respects, involves control (Principia Cybernetica Web)

14. The Concept of a Constraint • Limitations that preclude certain actions • May preclude achievement of certain objectives • Actions not precluded are “feasible” • Elastic (removable) vs. inelastic (not removable) constraints • “Binding” vs. “nonbinding” constraints

15. Leibig’s Law of the Minimum

16. Large Scale Systems • Costs • Numbers of people • Geographical or other extent of influence • Volume of information required to describe what is happening within the system • Number of interaction between components • Size of the potential for disaster • Extent of consequences of systems failure.

17. The Structure of a Concept • Underconceptualization: a problem in managing risk in large scale, complex systems • Concept: a cognitively significant word or phrase used in a proposition to represent some aspect(s) of a system • The division of a concept: C is a concept, D(C) is a division of C if D(C) partitions C into components (Warfield 1990)

18. Complexity maybeconceived as: “…..the predicament presented to the human mind when it encounters conceptualizations that exceed its unaided power to assess or evaluate.” John N. Warfield, Understanding Complexity: Thought and Behavior

19. Structural Underconceptualization • When managing risk in complex systems, the formal, logical structure of the system is usually not specified. Why? • Ignorance • Decision-makers have diverse beliefs about the situation (often do not even share same linguistic domain) • Organizational linguistics • Differing values

20. The Genesis of Value • What are values? • Are values controlled by hereditary instincts, genes, and personalities? • Are individuals’ values determined by society? • Are any values autonomously chosen? • Can a unified theory of risk evaluation ever be obtained without answering these questions?

21. A Key to Coherence in Managing Risk in Complex Systems • Recognition that there is an unavoidable trade off between simple assumptions and sophisticated models, and sophisticated assumptions and simple models • The former is more the norm (e.g. neoclassical and price theoretic models of economic systems all assume homo economicus; Marx’s model assumed communist man) • The latter is generally superior when dealing with large scale complex systems

22. Disparate Risks and Catastrophes • Disparate risks: a serious imbalance among outcomes (no matter probabilities) • A potential “catastrophe” exists when risks are so disparate that one outcome is “incomparably” greater than the alternatives • Rescher’s principle: All else equal, an action that might lead to a catastrophe makes that action ineligible (avoid any real catastrophe at any ordinary cost).

23. Insurance Against Catastrophe • Insurance as a device for reducing the prospect of substantial loss (a trade of hazards) • The value of (price the rationale actor is willing to pay for) an insurance policy depends upon the alternative actions in the choice set.

24. Risk Dilemmas • Sometimes, all of the alternative feasible actions entail catastrophe • In this case, the relative size of the catastrophe is the paramount factor • The distinction between catastrophe and acceptable risk is a judgmental issue (example of Japanese attack on Pearl Harbor, page 90, Rescher)

25. A “Fundamental Principle” of the Theory of Risk • Risk acceptability is always comparative, relative to the options, and thus unacceptability is never absolute, only relative • Implicit: the risk-free option is, at times, not an element in the choice set

26. Uncertainty • Uncertainty: indetermination through ignorance of otherwise, of some of the characterizing elements of a risk situation • Rescher’s three modal taxonomy: (a) Probability uncertainty (P-uncertainty) (b) Result uncertainty (R-uncertainty) (c) Outcome uncertainty (O-uncertainty)

27. A brief history of uncertainty • Plato: lack of trust in sense experience as a basis for knowledge • Rene Descartes: systematic doubt, and the search for a priori certainty • David Hume: the problem of induction • Wittgenstein: language games • Karl Popper: Induction

28. P-uncertainty • Probabilities cannot be reliably determined • Insufficient data to establish relative frequencies • Insufficient theories, models, or data to establish likelihoods (e.g. nuclear casks) • Subjective probabilities • What probabilities does one use when evaluating the risk of nuclear war?

29. R-uncertainty • One faces outcomes with imponderable values • Uncertainty about how to evaluate an outcome • How did Truman value the loss of life when deciding to bomb Japan?

30. O-Uncertainty • Scientific knowledge is inadequate to describe the nature of the hazard • Cannot fully specify range of outcomes • What outcomes will follow from the US military invasion of Iraq? • What is the national security risk attributable to US dependence upon Mid-Eastern oil?

31. The Ecology of Uncertainty: Sources, Indicators, and Strategies for Informational Uncertainty • Schunn, Kirschenbaum, and Trafton: effort to develop one taxonomy each for sources, indicators and strategies • Focus upon evaluation and management of uncertainty in three domains a) meteorological forecasting, b) fMRI data analysis c) submarine operations • Interest in informational uncertainty

32. Information • Shannon, “that which reduces uncertainty” • Bateson, “a difference that makes a difference” • “…. equivalent of or the capacity of something to perform organizational work, the difference between two forms of organization or between two states of uncertainty before and after a message has been received” Principia Cybernetica • Elementary unit in information theory: “bit,” a difference (e.g. between a 0 and a 1)

33. Sources of Informational Uncertainty • Physics uncertainty • Computational uncertainty • Visualization uncertainty • Cognitive uncertainty

34. “Physics Uncertainty” is uncertainty attributable to: • Absence of a signal • Noise/bias in the signal • Noise/bias in the way the signal is transduced

35. “Computational Uncertainty” is uncertainty attributable to: • Unpredicted [perhaps unpredictable?] future changes in the system • Statistical artifacts (e.g. the imposition of linearity, statistical assumptions, smoothing operations on data) • Fast and cheap: elaborate algorithms that require lots of time to run, even on high powered computers (more time than is available to the decision-maker)

36. “Visualization Uncertainty” is uncertainty attributable to: • Limits on the information that can be logically derived from a structural graphic • Sometimes information is missing from the structural graphic • Sometimes multiple dimensions are represented by composite indicators • Sometimes there are multiple graphics that contain conflicting information

37. “Cognitive Uncertainty” is uncertainty attributable to: • perceptual error (e.g. in encoding, storing, retrieving, or processing visual information from the measurement devices) • memory encoding error • information overload • retrieval error • background knowledge error • skill error

38. Uncertainty Tradeoffs • Tradeoffs evidently exist in which reductions in one source of uncertainty come at the cost of increases in other sources • Physical and computational uncertainty • Visualization and cognitive uncertainty • Memory and perception within cognitive uncertainty

39. Indicators of Uncertainty • See no or unusually weak patterns • See an impossible pattern • See a pattern that mismatches a known state of the world • See a pattern that is inconsistent across data sources

40. Strategies for Dealing with Uncertainty • Check likely errors • Focus on reliable sources • Adjust for known deviations from truth • Average across sources • Acquire more data • Gather better data • Bound uncertainty (satisfice)

41. Implications of “Deep Similarity” Between Domains • Taxonomies may be generalizable to other domains • Dissatisfaction with conventional distinction between “internal” and “external” uncertainty • Dissatisfaction with distinction between model errors and inherent stochastic variability • Minor role of linguistic uncertainty

42. Risk Management Strategies: The Interrelation of Rules • Three “cardinal rules” of risk management: • Maximize expected values • Avoid Catastrophes • Dismiss Extremely Remote Possibilities • The order of preference between them is: (3), (2), (1)

43. Risk Management Decisions • May be oriented toward outcomes or toward processes • Outcome orientation tends to be normative • Process orientation tends to be descriptive • Process orientation considers three stages: 1) Pre-decision stage 2) Decision stage 3) Post-decision post-decision *** Each stage is itself composed of a series of partial decisions ***

44. The Pre-decision Stage • Motivated by a sense of discontent or conflict attributable to difference between an ideal and the real world (a problem) • The decision-maker recognizes that the ideal alternative is infeasible (not in the choice set)

45. The Decision Stage: Sequences of Partial Decisions • Choice: a matter of imposing constraints upon the decision situation in such a way as to rule out alternatives • Alternative ways of binding the decision problem may be tried • The choice is made when constraints are imposed and allowed to bind upon decision situations until only one alternative remains • In Zeleny’s model, the operative rule is to thus select the alternative that comes closest to the ideal

46. Question I • Does the lack of an explanation of the genesis of value pose a serious threat to the orthodox theory of risk evaluation? If not, explain. If so, what in your view is the appropriate intellectual response?

47. Question II • Is there in your view a sense in which might one consider global security to be a matter of risk management? If so, in what way or ways do the basic concepts discussed today help to elucidate and examine it? If not, what alternative approach would you suggest other than risk management?

48. Question III • In your view, how realistic is the reasonable person standard at describing the psychological and social foundations of risk evaluation and management? Do you believe that risk managers really make autonomous decisions, or is all of the psychology involved in risk evaluation and management really social psychology?