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Making decisions under uncertainty is a major task, yet we face it in our everyday lives. Whether a decision is buying an umbrella in a cloudy day or funding a Mega-project, individuals make choices assisted by assumptions to address incomplete information based on values and experiences, moving through a strategy to reach a desired scenario.


In a corporate setting, decisions are constrained by a dominant logic enacted by senior management that package them in the organizational culture, normally following a “window of opportunity” that is circumscribed by preconceptions about how future events could materialize. Furthermore, two key variables, information and time, are crucial in decision-making: Increasing or decreasing any of them implies costs and returns. Therefore, the capability to decide will then depend on variables that are intrinsic to the individuals in an organization.


The Nobel Laureate Herbert Simon postulated that rationality is bounded primarily by the limitations of the human mind and the amount of information available, considering a specific objective and the expected cost of making a particular choice. In other words, these constraints, besides shaping the awareness of future scenarios, influences the perceptions of the external environment such as threats and opportunities to the organization.

To navigate uncertainty and simplify decision-making processes, individuals tend to take mental shortcuts, commonly known as heuristics. In other words, the “rules of thumb” individuals use based on “common sense” when attempting to solve a problem. Since heuristics derive from values and perceptions, trying to design a “standard way to decide" a utopia: decision-making processes and frameworks differ across individuals and organization, creating remarkable differences in the quality of choices that construct a strategy that reflect in the outcomes.

It is possible to increase the quality of our choices (hence our strategy) by understanding the elements that incline the "mental shortcuts" to a particular direction: In one of the most significant advances psychology and behavioural economics, Amos Tversky and Daniel Kahneman defined three heuristics and identified a set of biases that commonly affect decision-making. The following paragraphs briefly explains how these heuristics can be distorted by individual perspectives:

Heuristic 1: Representativeness: The judgment of the probability of an event based on the degree an event is representative of another. Biases to representativeness are: 

  • Insensitivity to prior probabilities of outcomes: Considering influencing information to predict an outcome instead of the true characteristics of the universe. In other words: stereotyping. Let’s say for instance that, in a group of 10 individuals 3 are top managers. An individual from this group that is seen wearing a suit in an expensive restaurant can be normally assumed as a top manager, regardless the fact that there is only 30% chance this is the case.


  • Insensitivity to sample size: Also called conservatism, consists in inferring the characteristics of a group by extrapolating the proportions of a sample. For instance, inferring that the average height of all men is 6 feet based on a sampling of 1,000 men in a particular region.


  • Misconceptions of chance: The belief that a sequence in a random process will represent the process itself. For instance, betting to a particular number in the lottery because it hasn’t appeared in previous draws. 


  • Insensitivity to predictability: Ignoring how predictable (or unpredictable) an outcome can be based on historical information (e.g.: the future price of a stock or commodity will be $X because it followed a determined pattern in past periods; a hockey team will win the match because it has won the last three matches) 


  • Illusion of validity: Overestimating (or underestimating) a judgment and the capacity to predict based on past outcomes. For instance, predicting the good (or bad) performance of a construction contractor based on continuous good (or bad) outcomes in the past, ignoring variables that are particular to the new project. 


  • Misconception of regression: Also represented by the effect known as regression to the mean, consists in defining the average characteristics of a group by extrapolating the averaging elements of a sub-group, often ignoring contextual factors. For instance, comparing intelligence of parents and sons, overestimating the effects of punishment and rewards based on modifications in the past performance of an employee.


Heuristic 2: Availability:  The judgment of frequencies of a class based on personal knowledge and experience. Biases to availability are: 

  • Retrievablity of instances: a class whose instances are easily retrieved will tend to increase the perception of occurrence over others that are not easily retrieved. In other words, a decision-maker will be inclined towards what he/she is more familiar with or what has seen more often: A person that sees a car accident will increase the probability of occurrence of another accident happening in the near future;  a manager who is exposed to project failures will be inclined towards “failure” as a normal outcome of projects.


  • Effectiveness of a search set: Focusing on the outcome of a search, ignoring the validity or robustness of the search itself. A specific context can lead to the favorable placement of some information that may be less relevant or numerous than other if the search is not conducted objectively. For instance, assuming the profitability of a product or a company based on a single marketing analytics report because it was readily available.


  • Imaginability: Weighting future scenarios based on the probability of occurrence of favorable or unfavourable outcomes in the past, which increases the risks of overestimating or underestimating a situation. This bias is commonly observed when corporations are setting long-term strategies that heavily rely on extrapolation of past experiences without considering contextual information or emerging trends.


  • Illusory correlation: When two events are perceived to happen together, the decision-maker will tend to wrongly associate them as interdependent, deriving a “story” from the data without considering the possibility of a "hidden" third variable. Common examples are assuming that cold weather cause people to shop more in malls, vaccines cause autism or that natural resources debilitates institutions in a country. It is essential to keep in mind the statistics mantra “correlation does not imply causation”.


Heuristic 3: Adjustment and Anchoring: When a relevant prediction is available, subsequent refinements are conducted to arrive to a final solution based on the initial value. Common biases are: 

  • Insufficient Adjustment: intuition can play a role on the way progressive adjustments are made based on an initial value or in the configuration of the data.  Furthermore, people tend to adjust minimally to get closer to the original value.  For instance, if somebody asks an analyst whether the oil price in 2020 will be more or less than $50/bbl, the will give one or other answer. If, subsequently, this analyst is asked to predict the price, the answer will tend to be close to $50/bbl 


  • Distorted evaluation of conjunctive and disjunctive events: People tend to overestimate the occurrence of conjunctive events, creating over optimism (such as the sequence of activities to complete a project) and underestimate the occurrence of a disjunctive event (such as the failure of a complex component in a process)


  • Anchoring in the assessment of subjective probability distributions: This bias consists in the assignment of an arbitrary probability of occurrence of an event based on personal beliefs or experience, without rigorous or formal analysis. This type of bias can heavily influence risk appraisal exercises, exposing organizations with insufficient mitigation strategies. 


Understanding how individuals bias criteria under uncertainty is essential for the integrity of the decision-making processes. However, it is unrealistic to pretend perfect heuristics: trying to cover all biases could lead to situations of “analysis paralysis” or loss of competitiveness, eroding the value generation potential of a strategy. Instead, it is necessary to generate a high level of awareness by clearly identifying them as risks to the decision-making process, setting clear paths to remove, mitigate or accept them.


Since biases are intrinsic to organizations, the combined effort of internal resources and external consultants is crucial to reach a level of awareness that increases robustness in the decision-making processes. For that reason, organizations have progressively realized the importance of incorporating specialized and independent perspectives when setting the long-term strategy.


Strategic consultants like Septentrion engineer and implement customized frameworks that guide decisions, analyzing scenarios and ensuring all assumptions are adequately evaluated. This allows extracting the maximum possible value while progressing towards the strategic goals, embracing adaptation when required and ensuring right decisions are taken right.



​References:

Simon, H. (1991) 'Bounded Rationality and Organizational Learning', Organization Science, 1 (1), pp. 125-134


Tversky, A. & Kahneman, D. (1974) ‘Judgement Under uncertainty: Heuristics and Biases’, Science, 185 (4157), pp. 1134-1131

 Are You Making Your Right Decisions Right? 

Cognitive Limitations in Decision-Making Processes

​David Tain, MSc., P.Eng., PMP

David received a MSc. in International Management (Oil and Gas concentration) from the University of Liverpool and completed the Strategic Decision and Risk Management Program at Stanford University. He obtained his Civil Engineering degree in 2001 from Santa Maria University in Venezuela and progressively advanced his education in Project Management, Project Development and Organizational Strategy at several institutions across the globe, remarkably Villanova University in USA and the Institut Français du Pétrole (IFP) in Paris. David is a Professional Engineer (P.Eng.) in the province of Alberta, Canada. He can be contacted at david.tain@septentrioncanada.com

 “We think, each of us, that we are much more rational than we are. And we think that we make our decisions because we have a good reason to make them. Even when it's the other way around. We believe in the reasons, because we've already made the decision".

Daniel Kahneman 

 About the Author:


David Tain, MSc., P.Eng., PMP is the Principal Consultant - Project Management & Strategy Execution at Septentrion Strategic Solutions. David has worked extensively in the development of industrial facilities primarily for the oil and gas sector, holding diverse project leadership positions in several international oil operators and multinational engineering and construction corporations in North and South America. His professional and academic expertise focuses on projects execution, strategic organization, decision analysis, leadership, negotiation and the analysis of human behaviour in project environments.