Behavioral Aspects in

Decision Analysis

Raimo P. Hämäläinen Systems Analysis Laboratory

Aalto University, School of Science Finland

S A L Systems Analysis Laboratory

”

The models in Decision Analysis relate to the preferences and utilities of the decision maker

Models are free of behavioral effects

As soon as we start using models in practice the behavioral elements are present

What is the human impact on model based decision and systems analysis?

Why should we consider behavioral aspects?

Howard Raiffa A pioneer in decision analysis

Expand

Do nothing

+$5,000,000

-$3,000,000

$2,000,000

-$1,000,000

Economic Expansion

Economic Contraction

Economic Expansion

Economic Contraction

This led to Decision Analysis (1968) • How to structure decision problems • Use of subjective probabilities • Decision trees

When teaching statistics, Raiffa learned that more is needed to support decision making in practice

Decision Analysis: A Personal Account of How It Got Started and Evolved

(Howard Raiffa, Operations Research 2002)

Acknowledged behavioral aspects already early • 60s challenge: How to obtain reliable judgments

from experts? • Research on improving elicitation procedures and

framing Behavioral perspective is essential in Decision Analysis

Ralph Keeney and Howard Raiffa (1976)

”These psychological insights will undoubtedly help analysts design better

assessment protocols in the future.”

Early suggestion: Ask elicitation questions in multiple ways • Check consistency and discuss with DM

Decision behavior

Gains

Per

ceiv

ed v

alue

Value function

Descriptive psychological research in judgment and decision making

Axioms of rationality not followed Cognitive and motivational biases • Anchoring, scope insensitivity, confirmation bias etc. Heuristics Systems 1 and 2 thinking

Human behavior drives the Decision Analysis process

Social interaction: Engagement, dialogue, communication

Behavioral effects are present in all the steps • Problem framing • Choice of criteria • Uncertainty modelling, etc.

Biases influence elicitation of subjective values and parameter estimates • Weighting • Estimation of consequences and probabilities

Textbooks discuss biases, decision traps and how to deal with them

Decision Analysis and Behavioral Research (Von Winterfeldt and Edwards 1986)

Making Hard Decisions (Clemen and Reilly 1999, 2014)

Structured Decision Making (Gregory et al. 2012)

Montibeller and von Winterfeldt (2015) review: • 175 references to papers related to biases

in DA • 30 biases and ideas for debiasing There is still a lot to be done!

Bias Explanation Debiasing

Anchoring A numerical value is based on an initial value (anchor), which is then insufficiently adjusted to provide the final answer.

Avoid anchors. Use different experts who use different anchors

Gain-loss bias Descriptions of a choice and its outcomes either as gains or as losses and may lead to different answers

Clearly identify the status quo.

Myopic problem representation

Oversimplified problem representation is adopted based on an incomplete mental model of the decision problem.

Explicitly encourage to think about more objectives, new alternatives.

Splitting biases The way the objectives are grouped in a value tree affects their weights or the way a fault tree is pruned affects the probabilities placed.

Use hierarchical estimation of weights or probabilities.

Proxy bias Proxy attributes receive larger weights than the respective fundamental objectives.

Avoid proxy attributes

Range insensitivity bias

Weights of objectives are not properly adjusted to changes in the range of attributes.

Make attribute ranges explicit. Use multiple elicitation procedures.

Montibeller and von Winterfeldt (2015).

Examples of biases in the DA process

Splitting bias

Higher weight if attribute is split into more detailed lower level attributes

Recreation

Nature

Economy

Dense bay vegetation

Shoreline vegetation

Economy

Reproduction of fish

Recreational fishing

Variation in water level

2/6

2/6

2/6

1/6

1/6

1/6

1/6

1/6

1/6

Occurs e.g. when people give equal weights to all attributes

No splitting bias vs. systematic bias

No splitting bias

𝑤𝑒𝑒𝑒𝑤𝑒𝑒𝑒𝑒

𝑤𝑒𝑒𝑒𝑤𝑒𝑒𝑒𝑒

𝑛𝑒𝑒𝑒𝑛𝑒𝑒𝑒𝑒

𝑛𝑒𝑒𝑒𝑛𝑒𝑒𝑒𝑒

Systematic bias

The effect of splitting the environmental attribute

Students with debiasing guidance: no splitting bias

Stakeholders: systematic bias, guidance did not help

Splitting bias is difficult to eliminate

Hämäläinen and Alaja (2008)

Ideas to deal with biases still need to be tested in practice

Guidance • Did not help to avoid the splitting bias (Hämäläinen and Alaja 2008) • Pre-exposure to attribute levels can reduce decision context related

biases (Carlson and Bond 2006)

Estimate bias coefficients and calibrate judgments • To reduce scale compatibility bias (Anderson and Hobbs 2002) • To reduce loss aversion bias (Bleichrodt et al. 2001)

Design procedures so that the effects of biases cancel out (Lahtinen and Hämäläinen 2015) • Does not require additional training or calibration

To ”bridge the gap between how people actually do make decisions… and how they should make decisions”

• PrOACT framework Problem: Define it Objectives: Identify and clarify them Alternatives: Develop good alternatives Consequences: Describe alternatives Trade-offs: Make tough compromises

• Even Swaps method

• Avoiding psychological traps discussed

Decision Analysis for the general public: Smart Choices (1999)

The Even Swaps process Eliminate attributes by irrelevance and alternatives by dominance until one alternative remains

• Even Swap: Alternative replaced by a preferentially equivalent one that differs in two attributes

• The process can be technically challenging

• Smart Swaps software helps to identify efficient swaps (Mustajoki and Hämäläinen 2007)

Dominated by

Lombard

Practically dominated

by Montana

78

25

An even swap Commute time

removed as irrelevant

Behavioral phenomena in Even Swaps

The process allows different paths: Sequences of swaps taken

Accumulation of biases is possible and can depend on the path followed

Path dependence when different paths lead to different outcomes

DM chooses A?

DM chooses B?

The measuring stick effect can create path dependence

(Lahtinen and Hämäläinen 2015)

Pricing path Make all attributes but cost irrelevant. Use money (cost) as measuring stick in all swaps.

Explanation Money as measuring stick in all swaps: Alternatives with low cost are favored

Debiasing Avoid using measuring stick in which alternatives differ much

Apartment related case: Four alternatives, pricing path and two reference paths

Behavioral challenges in group decision making

Risk of biases is high Strategic behavior • Stakeholders can emphasize factors that are

important to them • Strategic representation of preferences Groupthink (Janis 1972)

Facilitator skills important: Understand and manage behavior in the social system

This is the right model

Yes

Yes

Yes

Yes Yes

Policy action prioritization in Egypt (2012) • DA workshop facilitated by CMI (Finland) • Overall goal: Mitigation of violent conflicts • Participants: Government officials

DA tools also used in regional conflicts in the Middle - East

Decision Analysis used to solve conflicts

Established by president Martti Ahtisaari, Nobel Peace Price 2008

Howard Raiffa A pioneer in negotiation science

Active in the negotiation on IIASA in the cold war period

Negotiation course at Harvard in 1970s • Demonstrated that student experiments can be valuable for

developing theory

”Collectively we could test what worked…and …discuss whether our heuristic insights would be applicable in the real world” (Raiffa 2002)

⇒ The Art and Science of Negotiation (1982) • Analytical models and Behavioural insights

Camp David negotiations (1970s) • Between Egypt (Anwar El Sadat) and Israel

(Menachem Begin) • Mediated by the US (Jimmy Carter) • A sequence of single negotiation texts with joint gains

(Roger Fischer)

Negotiation in conflict resolution

Begin, Carter and El Sadat

Israel

Egy

pt

Reservation value

Reservation value

The ART of negotiation emphasizes behavioral aspects

Joint gains process: Searching for solutions where each player gains and need not make trade-offs

Behavioral challenges:

• Effect of the starting point • Strategic misrepresentation of preferences The idea of post-settlement settlements: • Negotiations after initial settlement • Mediator suggests a post-settlement with joint gains

Behavioral perspective in modeling

Behavioral Decision Analysis can inform general modeling studies

What is the human impact on the modeling process? Modeler biases, communication, group interaction etc. Man and the hammer – syndrom

Behavioral Operational Research is an emerging area (Hämäläinen et al. 2013)

Upcoming special issue in (Franco and Hämäläinen 2015)

Emotions are needed in decision making

People with damaged emotion related brain areas have difficulties in decision making (Damasio 1994)

Negative emotions can lead to avoiding decision making, sticking with status quo (Luce 1998)

Frustration and anger increases risk taking behavior (Leith and Baumeister 1996)

Future? Decision neuroscience

Insula activation (Preusschoff et al. 2008)

Does it help to understand the neural processes?

Different areas of the brain involved in different tasks Striatum related to reward evaluation • Striatum is a subcortical part • In the brain area that was developed very

early in human evolution Insula related to risk evaluation • Insula is located on brain cortex • Developed later in evolution

Striatum activation (Niv and Montague 2009)

Howard Raiffa acknowledged the importance of behavioral aspects early on

Today: Wide awareness of biases within the decision analysis community • Challenge to spread awareness to practitioners in

different areas, e.g. in environmental management and policy decision making

• Debiasing methods need to be tested and taken into practice

Behavioral perspective is important in the entire modelling and systems analysis process

Summary

References Bleichrodt, H, J.L. Pinto, and P.P. Wakker. Making descriptive use of prospect theory to improve the prescriptive use of expected utility. Management science 47(11), 2001: 1498-1514.

Carlson, K. A., and S. D. Bond. Improving preference assessment: Limiting the effect of context through pre-exposure to attribute levels. Management Science 52(3), 2006: 410-421.

Damasio, A. R. Descartes' error and the future of human life. Scientific American 271(4), 1994.

Delquié, P. Inconsistent trade-offs between attributes: New evidence in preference assessment biases. Management Science 39(11), 1993: 1382-1395.

Franco, L. A. and R.P. Hämäläinen. Behavioural operational research: Returning to the roots of the OR profession. European Journal of Operational Research, In Press, 2015. http://www.sciencedirect.com/science/article/pii/S0377221715009601

Hammond, J. S., R. L. Keeney, and H. Raiffa. Smart choices: A practical guide to making better decisions. Vol. 226. Harvard Business Press, 1999.

Hämäläinen R.P. , J. Luoma and E. Saarinen: On the Importance of Behavioral Operational Research: The Case of Understanding and Communicating about Dynamic Systems European Journal of Operational Research, 228 (3), (2013): 623-634.

Hämäläinen R.P.: Behavioural issues in environmental modelling - the missing perspective. Environmental Modelling & Software, 73, 2015: 244-253.

Hämäläinen R.P. and T. J. Lahtinen: Path Dependence in Operational Research - How the Modeling Process Can Influence the Results. 2015. Manuscript: http://sal.aalto.fi/publications/pdf-files/mham15c.pdf

Hämäläinen, R.P., S. Alaja: The Threat of Weighting Biases in Environmental Decision Analysis Ecological Economics, 68, 2008: 556-569.

Janis, I. L. Groupthink: Psychological studies of policy decisions and fiascoes. Boston: Houghton Mifflin. 1982.

Keeney, R. L., and H. Raiffa. Decisions with multiple objectives: preferences and value trade-offs. Cambridge university press, 1993.

References Lahtinen T.J. and R.P . Hämäläinen: Path Dependence and Biases in the Even Swaps Decision Analysis Method. European Journal of Operational Research, 2015, In Press. http://sal.aalto.fi/publications/pdf-files/mlah15.pdf

Leith, K. P., and R. F. Baumeister. Why do bad moods increase self-defeating behavior? Emotion, risk tasking, and self-regulation. Journal of personality and social psychology 71(6), 1996.

Liesiö J., P. Mild, and A. Salo: Robust Portfolio Modeling with Incomplete Cost Information and Project Interdependencies European Journal of Operational Research, 190, 679-695.

Luce, M. F. Choosing to avoid: Coping with negatively emotion-laden consumer decisions. Journal of consumer research 24(4), 1998: 409-433

Montibeller, G., and D. Winterfeldt. Cognitive and Motivational Biases in Decision and Risk Analysis. Risk Analysis, 2015.

Mustajoki J. and R.P.Hämäläinen: Web-HIPRE: Global decision support by value tree and AHP analysis INFOR, 38, no. 3, 2000, 208-220

Mustajoki J. and R.P. Hämäläinen: Smart-Swaps - A decision support system for multicriteria decision analysis with the even swaps method Decision Support Systems, Vol. 44, No. 1, 2007, pp. 313-325. http://smart-swaps.aalto.fi/

Niv, Y., and P. R. Montague. Theoretical and empirical studies of learning. Neuroeconomics: Decision making and the brain, 2008: 329-50.

Preuschoff, K., S. R. Quartz, and P. Bossaerts. Human insula activation reflects risk prediction errors as well as risk. The Journal of neuroscience 28(11), 2008: 2745-2752.

Raiffa, H. Decision analysis: a personal account of how it got started and evolved. Operations Research 50(1), 2002: 179-185.

Raiffa, H. The art and science of negotiation. Harvard University Press, 1982.

Raiffa, H. Decision analysis: introductory lectures on choices under uncertainty. 1968.

Raiffa, H. Post-settlement settlements. Negotiation Journal 1(1), 1985: 9-12.

Rilling, J. K., and A. G. Sanfey. The neuroscience of social decision-making. Annual review of psychology 62 (2011): 23-48.