3  Decision-making Frameworks and Behavioral Decision Making

3.1 Cost-benefit analysis and net present value

Presentation link

Cost-benefit analysis (CBA) and net present value (NPV) are both essential financial assessment tools used in project evaluation and investment decision-making.

  • These methodologies are applied to understand the feasibility, profitability, and financial viability of projects or investments over time.

  • While both are rooted in the principles of finance and economics, they serve slightly different purposes and offer unique insights into the financial health and potential of projects.

3.1.1 Cost-Benefit Analysis (CBA)

  • Cost-benefit analysis is a systematic approach used to evaluate the economic strengths and weaknesses of a project by quantifying its costs and benefits in monetary terms.
  • The main objective of CBA is to determine if the project is worthwhile from an economic perspective—that is, whether its benefits outweigh its costs.
  • This method is widely used in public policy, project management, and for making business decisions.

Process of Cost-Benefit Analysis:

  1. Identification of Costs and Benefits: This includes direct and indirect, as well as tangible and intangible factors. Direct costs might be capital expenses, while indirect benefits could include improved public health or environmental benefits.

  2. Quantification of Costs and Benefits: All identified factors are assigned monetary values, often requiring the use of estimation and forecasting techniques.

  3. Time Adjustment: Since costs and benefits occur at different times, they are adjusted to their present value using a discount rate. This makes future benefits and costs comparable to present values.

  4. Comparison and Decision Making: The net benefit of the project is calculated by subtracting total costs from total benefits. Projects with a positive net benefit are typically considered viable.

3.1.2 Net Present Value (NPV)

  • Net present value is a financial metric used to assess the profitability of an investment or project by calculating the difference between the present value of cash inflows and the present value of cash outflows over a period of time.
  • NPV is rooted in the principle of time value of money, recognizing that a dollar today is worth more than a dollar in the future due to its potential earning capacity.

Calculating NPV:

  1. Estimate Future Cash Flows: Estimate the expected cash inflows and outflows associated with the project or investment over its lifetime.

  2. Select a Discount Rate: This rate reflects the opportunity cost of capital, the risk of the investment, and inflation. It is used to discount future cash flows to their present value.

  3. Calculate Present Value of Cash Flows: Each future cash flow is discounted back to its present value using the selected discount rate.

  4. Determine NPV: Subtract the present value of cash outflows (investments) from the present value of cash inflows. A positive NPV indicates that the project or investment is expected to generate value over its lifecycle, making it a potentially good investment.

3.1.3 Comparison and Interplay

While both CBA and NPV are used for evaluating the financial viability of projects, there are key differences:

  • Scope: CBA takes a broader view, considering the overall economic impact, including non-monetary benefits and costs. NPV focuses strictly on the financial returns of an investment.

  • Application: CBA is commonly used in public policy and environmental projects where benefits and costs extend beyond mere financial gains to societal impacts. NPV is more prevalent in corporate finance and investment analysis for its focus on profitability.

  • Outcome Interpretation: CBA results in a ratio or a net benefit figure, indicating the economic value of a project beyond its costs. NPV delivers a dollar amount representing the excess or shortfall of cash flows, discounted to their present value, suggesting direct financial return.

  • In practice, combining CBA with NPV provides a comprehensive view of a project’s worth, considering both its broader economic impact and its potential to generate financial returns.

  • This dual approach helps decision-makers assess the viability and desirability of projects from multiple dimensions, leading to more informed and holistic investment decisions.

3.2 Example - Cost-benefit analysis and net present value

Let’s consider the example of a local government evaluating a new public park project to illustrate how cost-benefit analysis (CBA) and net present value (NPV) are used in decision-making.

3.2.1 Project Overview

  • The local government is considering developing a new park in a suburban area. The park is expected to improve quality of life by providing recreational space, enhancing environmental conditions, and potentially boosting property values in the area.
  • The project’s estimated development cost is $5 million, including land acquisition, landscaping, playground equipment, and facilities.
  • The park’s benefits, including public health improvements, increased property values, and environmental benefits, are more challenging to quantify but are significant factors in the decision-making process.

3.2.2 Applying Cost-Benefit Analysis (CBA)

  1. Identification of Costs and Benefits:
    • Costs: Land acquisition ($2 million), construction and landscaping ($2.5 million), maintenance ($500,000 annually).
    • Benefits: Increased property taxes due to higher property values, health benefits from increased physical activity, and environmental benefits like improved air quality.
  2. Quantification of Costs and Benefits:
    • It’s estimated that the park will increase surrounding property values, leading to an additional $300,000 in property taxes annually. Health improvements and environmental benefits are valued at $200,000 annually, based on studies of similar projects.
  3. Time Adjustment:
    • Assuming a discount rate of 5% to account for the time value of money.
  4. Comparison and Decision Making:
    • The net benefit is calculated by summing the present value of all benefits and subtracting the present value of all costs over a defined period, say 20 years.

3.2.3 Applying Net Present Value (NPV)

  1. Estimate Future Cash Flows:
    • Cash inflows: Additional property taxes of $300,000 annually, plus estimated benefits (health and environmental) quantified at $200,000 annually.
    • Cash outflows: Initial investment of $5 million, with ongoing maintenance costs of $500,000 annually.
  2. Select a Discount Rate:
    • A discount rate of 5% is chosen, reflecting the opportunity cost of capital and the project’s risk level.
  3. Calculate Present Value of Cash Flows:
    • Each year’s net cash flow (inflows minus outflows) is discounted back to its present value using the 5% rate.
  4. Determine NPV:
    • The NPV of the project is calculated by summing the present values of all net cash flows, including the initial investment cost.

3.2.4 Example Calculation

For simplicity, let’s do a basic NPV calculation for the first 5 years (ignoring the annual increase in benefits for ease of calculation):

  • Initial cost: -$5,000,000
  • Annual net inflow (property taxes + quantified benefits - maintenance): $300,000 + $200,000 - $500,000 = $0 (simplified for example purposes)

With no net inflow due to simplification, the NPV calculation focuses on the initial investment. In a real scenario, net inflows would typically be positive, and we would discount each year’s inflow back to its present value and subtract the initial investment.

  • For this simplified example, the NPV would be negative due to the initial cost with no offsetting net inflows.
  • However, in a real CBA, the quantified benefits would likely make annual net inflows positive, potentially leading to a positive NPV over a longer analysis period, indicating the project could be financially viable and beneficial to the community.

3.2.5 Summary

  • In this example, CBA helps understand the project’s broader economic and social impacts, while NPV provides a direct financial assessment.
  • While our simplified example doesn’t show a positive financial return due to the simplistic approach to net inflows, in reality, including quantifiable benefits often shows public projects like parks can have a positive NPV, justifying their development from both an economic and financial perspective.
  • This dual analysis allows decision-makers to consider both the tangible and intangible benefits of public projects, leading to more informed and comprehensive decision-making.

3.3 Game Theory and Strategic Decision Making

Game theory is a theoretical framework for conceiving social situations among competing players. It models the strategic interaction between two or more players in a situation with set rules and outcomes.
- Widely used across various disciplines such as economics, political science, psychology, logic, and biology, its applications span understanding competitive business strategies, evolutionary biology, and even the design of artificial intelligence.

3.3.1 Example Problem: The Prisoner’s Dilemma

One of the most renowned examples of game theory, the Prisoner’s Dilemma, demonstrates why two rational individuals might not cooperate, even if it appears in their best interest.

3.3.2 Scenario

  • Two criminals are arrested and imprisoned.
  • Each is in solitary confinement, unable to communicate with the other.
  • The prosecutors lack sufficient evidence for a principal charge but aim to sentence both to a year in prison on a lesser charge.
  • They offer each prisoner a bargain: betray the other and testify that the other committed the crime, or cooperate by remaining silent. The outcomes vary based on their decisions:
  • If both betray, each serves 2 years.
  • If one betrays while the other remains silent, the betrayer is freed, and the silent serves 3 years.
  • If both remain silent, each serves only 1 year.

To analyze this dilemma, we set up a payoff matrix for each decision:

Prisoner B / Prisoner A Betray Remain Silent
Betray -2, -2 0, -3
Remain Silent -3, 0 -1, -1
  • The first number in each cell represents Prisoner A’s years in prison, and the second number represents Prisoner B’s years in prison. For example, if A betrays B, but B remains silent, the cell for (Betray, Remain Silent) would have (0, -3), meaning A goes free and B serves 3 years. Solution Analysis:

Dominant strategy

  • Analyzing the payoff matrix, each prisoner has a Dominant strategy to betray the other, because betraying the other player gives a better payoff regardless of what the other player decides.
  • If one assumes the other will remain silent, betraying minimizes their own sentence. If one assumes the other will betray, betraying again minimizes their own sentence compared to remaining silent.

Nash Equilibrium

  • Thus, the Nash Equilibrium in this scenario is for both prisoners to betray each other, leading to both serving 2 years in prison. This outcome is not optimal for the collective (they would both get less time if they remained silent), but it is the stable outcome given the structure of the game.

3.4 Strategic Decision Making with Game Theory

In strategic decision-making, game theory offers a structured method to predict outcomes in strategic interactions. It helps decision-makers consider not just their own actions and payoffs but also how their decisions interact with those of others.

Examples and Applications:

  1. Competitive Markets: Firms compete by setting prices, quantities, or investing in advertising. Game theory models such as Cournot (competition in quantities) and Bertrand (competition in prices) help predict outcomes of such competitive strategies.
  2. Negotiation and Bargaining: Game theory outlines how parties can reach an agreement that benefits both sides, identifying strategies that optimize outcomes for all involved.
  3. Auctions: Different auction formats can lead to different bidding strategies and outcomes. Game theory helps in understanding the optimal strategies for bidders and the design of the auction itself.
  4. Voting and Political Strategy: The theory can predict how different electoral rules affect the strategies of voters and political parties.
  5. Public Goods and Common Resources: Game theory explains the dynamics of cooperation and conflict in the provision and consumption of public goods and common resources.

3.4.1 Strategic Considerations

  1. Anticipating Others’ Moves: Understanding potential moves by competitors or partners can influence one’s strategic choices. This includes predicting reactions to one’s actions in competitive environments.
  2. Commitment and Credibility: Sometimes, making a commitment to a certain strategy (like investing in capacity or setting a price) can influence the outcomes by shaping the expectations and responses of others.
  3. Mixed Strategies: In some situations, keeping opponents guessing by randomizing between different strategies can be beneficial, especially in competitive scenarios where predictability can be exploited.
  4. Sequential Moves and Forward Thinking: In sequential games, the ability to think ahead and plan for future moves can provide a strategic advantage. This involves backward induction, starting from the end of the game and working backward to determine the optimal strategy.

3.4.2 Limitations and Critiques

  • While game theory provides powerful tools for analyzing strategic interactions, its applicability depends on the accuracy of the assumptions regarding rationality, payoff structures, and information availability.
  • Real-world complexities, incomplete information, bounded rationality, and dynamic changes often challenge the predictive power of game-theoretical models.
  • However, even with these limitations, game theory remains a critical tool in the strategic decision-maker’s toolkit, offering insights into the structure of strategic interactions and guiding the formulation of effective strategies.

3.4.3 Summary

  • Game theory enriches the field of strategic decision-making by providing a framework to analyze the interactions among rational decision-makers.
  • It helps in identifying optimal strategies in competitive and cooperative settings, understanding the dynamics of conflict and negotiation, and designing mechanisms for better outcomes.
  • As such, game theory stands as a cornerstone of modern economic and strategic reasoning, guiding decision-makers through the complex landscape of interactive decisions.

3.5 Decision-making under risk and uncertainty

Decision-making under risk and uncertainty is a critical aspect of both personal and managerial decision-making processes, where the outcomes of choices cannot be predicted with certainty due to the inherent presence of unknown or unpredictable elements.
- This area of decision-making is particularly relevant in finance, business strategy, operations management, and various other fields where decisions must be made despite incomplete information about future states of the world.
- Understanding the difference between risk and uncertainty, as well as the strategies and tools available to navigate these conditions, is essential for effective decision-making.

3.5.1 Risk vs. Uncertainty

Risk refers to situations where the probabilities of different outcomes are known or can be estimated. Decision-makers are aware of the potential outcomes and the likelihood of each. For example, when investing in stocks, the historical volatility of returns can provide a measure of risk.

Uncertainty, on the other hand, describes conditions where the probabilities of outcomes cannot be known. Decisions under uncertainty involve unknown unknowns; for example, launching a new product in an entirely new market where there’s little to no historical data to gauge potential success.

3.5.2 Decision-making under Risk

When making decisions under risk, individuals and organizations often rely on statistical and probabilistic models to assess the likelihood of various outcomes. Common approaches include:

  1. Expected Value Calculation: Combining the potential outcomes with their respective probabilities to determine the most likely average outcome.
  2. Risk Analysis and Modelling: Using techniques such as Monte Carlo simulations to understand the range of possible outcomes and their probabilities.
  3. Risk Management Strategies: Including risk avoidance, risk reduction, risk sharing, and risk retention, to manage potential negative impacts on the organization.
  4. Portfolio Diversification: In finance, spreading investments across various assets to reduce exposure to any single risk.
  5. Decision Trees and Sensitivity Analysis: Tools that help visualize and analyze decisions under risk by considering different scenarios and their impacts.

3.5.3 Decision-making under Uncertainty

Under uncertainty, where probabilities are unknown, decision-makers employ different strategies:

  1. Scenario Planning: Developing a range of possible scenarios based on different assumptions and planning how to respond to each.
  2. Minimax Criterion: Choosing the option with the least-worst outcome to minimize potential losses in the worst-case scenario.
  3. Maximax Criterion: Opting for the decision with the highest potential upside, suitable for highly optimistic decision-makers.
  4. Real Options Analysis: Treating investments as options, where future decisions are made once more information becomes available, allowing for flexibility in response to unfolding events.
  5. Robust Decision Making (RDM): Focusing on strategies that perform well across a wide range of unpredictable future states, emphasizing resilience and flexibility.

3.5.4 Bridging the Gap between Risk and Uncertainty

In practice, decision-makers often face a combination of risk and uncertainty, necessitating a hybrid approach that incorporates elements from both paradigms. This may involve:

  • Gathering More Information: Conducting market research, feasibility studies, or consulting experts to reduce the level of uncertainty.
  • Adaptive Strategies: Implementing flexible strategies that can be adjusted as more information becomes available or as the situation evolves.
  • Fostering Innovation and Learning: Encouraging a culture that values innovation and learning can help organizations adapt to changes and uncertainties more effectively.

3.5.5 Summary

  • Decision-making under risk and uncertainty requires a careful assessment of available information, an understanding of the potential outcomes, and the adoption of strategies that align with the decision-maker’s goals and risk tolerance.
  • By employing appropriate analytical tools and strategies, individuals and organizations can navigate these challenging decision-making environments more effectively, making choices that balance potential rewards with acceptable levels of risk and uncertainty.
  • The dynamic nature of risk and uncertainty also underscores the importance of continual learning, adaptation, and resilience in the face of unforeseeable challenges.

3.6 Cognitive biases and heuristics

3.6.1 Cognitive Biases

  • Cognitive biases are systematic patterns of deviation from norm or rationality in judgment, whereby inferences about other people and situations may be drawn in an illogical fashion.
  • Biases can result from various sources, including the heuristics we use, emotional and moral motivations, or the limited processing ability of the human brain.
  • These biases often serve as a way to simplify information processing but can lead to perceptual distortion, inaccurate judgment, illogical interpretation, or what is broadly called irrationality.

Examples of Cognitive Biases:

  • Confirmation Bias: The tendency to search for, interpret, favor, and recall information in a way that confirms one’s preexisting beliefs or hypotheses, while giving disproportionately less consideration to alternative possibilities.
  • Anchoring Bias: The common human tendency to rely too heavily on the first piece of information offered (the “anchor”) when making decisions.
  • Overconfidence Bias: When someone’s subjective confidence in their judgments is greater than their objective accuracy.
  • Availability Heuristic: A mental shortcut that relies on immediate examples that come to a given person’s mind when evaluating a specific topic, concept, method or decision.
  • Loss Aversion: The tendency to prefer avoiding losses to acquiring equivalent gains; the pain of losing is psychologically about twice as powerful as the pleasure of gaining.

3.6.2 Heuristics

Heuristics are simple, efficient rules, either hard-coded by evolutionary processes or learned, which have been proposed to explain how people make decisions, come to judgments, and solve problems when facing complex problems or incomplete information. They serve as quick ways to solve problems or make judgments, but they can lead to errors or biases.

Examples of Heuristics:

  • Representativeness Heuristic: Assessing similarity or probability based on the resemblance to a typical case. It can lead to neglect of the base rate and the gambler’s fallacy.
  • Availability Heuristic: Estimating the likelihood of events based on their availability in memory; if instances come readily to mind, we presume such events are common.
  • Affect Heuristic: The process of making a decision based on emotions, where positive or negative feelings associated with a stimulus influence the perception of risk or benefit.

3.6.3 Implications of Biases and Heuristics

Cognitive biases and heuristics have profound implications across various fields:

  • Decision Making: They can lead to poor choices in personal finance, health, and professional settings.
  • Business and Economics: Marketers and economic analysts need to understand biases to predict consumer behavior and make better economic models.
  • Policy Making: Recognizing these biases is crucial for developing policies that can nudge people towards better decisions for themselves and society.

3.6.4 Mitigating Biases and Heuristics

While completely eliminating biases and heuristics from human decision-making may not be feasible, there are strategies to mitigate their impact:

  • Awareness and Education: Understanding and recognizing biases is the first step toward mitigating their effects.
  • Decision Aids: Checklists, algorithms, and other tools can help reduce reliance on biased thinking and heuristics.
  • Diverse Teams: Including individuals with diverse perspectives can help counteract individual biases.
  • Structured Decision-Making Processes: Encouraging critical thinking and structured approaches to decision making can reduce the influence of biases.

3.6.5 Summary

  • Cognitive biases and heuristics play a significant role in how we perceive the world and make decisions.
  • While they can lead to errors and irrationality, they are also adaptations to the complex nature of human cognition and decision-making.
  • By understanding these concepts, individuals and organizations can develop strategies to minimize negative impacts and improve decision-making processes, leading to better outcomes in a variety of contexts.

3.7 Examples - Cognitive biases and heuristics

Understanding cognitive biases and heuristics through examples can illuminate how these mental shortcuts and errors influence our everyday decision-making and perception. Here’s a closer look at some of the key biases and heuristics with relevant examples:

3.7.1 Cognitive Biases

  1. Confirmation Bias
    • Example: When researching online for opinions about a political issue, you primarily click on articles that align with your existing beliefs, ignoring articles that present opposing viewpoints. This reinforces your current belief without considering contrary evidence.
  2. Anchoring Bias
    • Example: During salary negotiations, the first number that gets mentioned acts as an anchor. If an employer offers $50,000 initially, all further negotiations are likely to revolve around that figure, regardless of whether it’s high or low relative to market rates.
  3. Overconfidence Bias
    • Example: A project manager estimates that a project will be completed in six months based on their judgment, without accounting for potential delays or unexpected issues. This overconfidence can lead to underestimating the time and resources needed, potentially causing project overruns.
  4. Availability Heuristic
    • Example: After hearing about several airplane accidents on the news, you might overestimate the risk of flying. The ease with which these tragic events come to mind makes them seem more common than they actually are, affecting your travel decisions.
  5. Loss Aversion
    • Example: Imagine you’re playing a game where you can either securely receive Rs.50 or have a 50% chance to win Rs.100. Many people will choose the secure $50, avoiding the risk of losing, even though both options have the same expected value. This demonstrates a preference for avoiding losses over acquiring equivalent gains.

3.7.2 Heuristics

  1. Representativeness Heuristic
    • Example: If you meet a slim, bespectacled individual who likes poetry, you might quickly conclude that they’re more likely to be a university professor than a truck driver, based on stereotypes, even though statistically, truck drivers vastly outnumber university professors.
  2. Availability Heuristic
    • Example: After your neighbor’s house is burglarized, you might overestimate the crime rate in your neighborhood and consider it unsafe, because the incident is fresh in your memory and easily comes to mind.
  3. Affect Heuristic
    • Example: Suppose you are deciding between two cars: one is from a brand that recently had a highly publicized recall, and the other is from a brand with no recent negative news. You might feel more positive about the second brand and choose it, even if objective data (like safety ratings) suggest the first car is a better choice. Your decision is swayed by your emotions rather than rational analysis.

3.8 Prospect theory and loss aversion

Prospect Theory, developed by Daniel Kahneman and Amos Tversky in 1979, represents a pivotal shift in the understanding of how people make decisions under risk and uncertainty, challenging the traditional expected utility theory (EUT).

3.8.1 Foundations of Prospect Theory

Prospect Theory posits that when individuals make decisions, they do so based on the potential value of losses and gains rather than the final outcome. Moreover, they evaluate these losses and gains using certain heuristics. The theory is structured around several key concepts:

  • Reference Points: People evaluate outcomes relative to a reference point (usually their current situation), rather than in terms of total wealth or absolute outcomes.
  • Loss Aversion: Individuals tend to prefer avoiding losses over acquiring equivalent gains. The pain of losing is psychologically about twice as powerful as the pleasure of gaining.
  • Diminishing Sensitivity: The theory also suggests diminishing sensitivity to both gains and losses. This means that the difference between $0 and $100 feels more significant than the difference between $100 and $200.
  • Probability Weighting: People tend to overweight small probabilities and underweight moderate to high probabilities, leading to risk-averse behavior in choices involving sure gains and risk-seeking behavior in choices involving sure losses.

3.8.2 The Value Function

Prospect Theory introduces a value function that is defined on deviations from a reference point, typically shaped as an “S”. This function is concave for gains, reflecting risk aversion, and convex for losses, reflecting risk-seeking behavior in losses. The function is steeper for losses than for gains, which visually represents loss aversion.

3.8.3 Loss Aversion

Loss aversion is a cornerstone of Prospect Theory and one of its most widely observed and influential concepts. It implies that the disutility of giving up an object is greater than the utility associated with acquiring it. For instance, losing 100 dollars is more painful than the pleasure derived from gaining 100 dollars. This concept has extensive applications, including in financial decision-making, marketing strategies, negotiation processes, and policy-making.

3.8.4 Applications and Implications

  • Financial Decision-Making: Loss aversion helps explain why investors might irrationally hold on to losing stocks to avoid realizing a loss, or why they might be reluctant to invest in assets with higher volatility, even if the long-term expected return is favorable.
  • Marketing Strategies: Marketers use loss aversion to their advantage by framing products or services in terms of what consumers stand to lose by not purchasing, rather than what they gain.
  • Negotiations: Understanding loss aversion can be crucial in negotiations, where offers can be framed in terms of loss prevention to make them more appealing.
  • Policy Making: Policies aimed at encouraging certain behaviors (like saving for retirement or reducing energy consumption) can be more effective when they leverage loss aversion, such as by highlighting the potential losses from inaction.

3.8.5 Criticisms and Limitations

  • While Prospect Theory has been immensely influential, it is not without its critics. Some argue that its empirical foundations, particularly around the specific shape of the value function and the extent of loss aversion, can vary significantly across different contexts and populations.
  • Additionally, there’s ongoing debate about how static the reference points are and how they shift over time or in response to other factors.

3.8.6 Summary

  • Prospect Theory and the concept of loss aversion have fundamentally changed our understanding of decision-making under risk and uncertainty.
  • By recognizing that decisions are influenced more by the potential for losses than gains, and that people evaluate outcomes based on changes from a reference point rather than absolute outcomes, we gain insights into a wide range of human behaviors and preferences.
  • This theory underscores the complexity of human psychology in economic decisions and continues to influence research, policy, and practice across multiple fields.

3.9 Examples - Prospect theory and loss aversion

To elucidate the concepts of Prospect Theory and loss aversion more concretely, let’s explore practical examples that demonstrate how these principles manifest in everyday decisions and scenarios.

3.9.1 Example of Prospect Theory

Scenario: Choosing Between Sure Gain and Probable Gain

Imagine you are given two options: 1. Option A: Receive $50 guaranteed. 2. Option B: A 50% chance to win $100 and a 50% chance to win nothing.

According to traditional expected utility theory, both options have the same expected value of $50. However, Prospect Theory suggests that most people would choose Option A, the sure gain, due to loss aversion and the overweighting of certain outcomes. This choice illustrates how people tend to be risk-averse when it comes to gains, preferring a certain outcome over a gamble with an equivalent expected value.

3.9.2 Example of Loss Aversion

Scenario: Losing $50 vs. Gaining $50

Imagine two scenarios: 1. Losing $50: You lose $50 that you had in your wallet. 2. Finding $50: You find $50 on the street.

  • While the monetary outcome of these scenarios is objectively neutral when combined (you’re neither richer nor poorer if both happen), loss aversion suggests that the pain of losing 50 dollars is significantly more intense than the pleasure of finding 50 dollars.
  • Thus, if you were to experience both events, you would likely still feel worse off, despite the monetary balance being zero. This is because the emotional impact of a loss is greater than that of a gain of the same amount.

3.9.3 Example of Reference Points

Scenario: Salary Expectations

Imagine you’re expecting a $5,000 bonus at the end of the year based on your performance, which sets your reference point. If you receive only $3,000, you might feel disappointed or even as if you’ve lost $2,000 relative to your expectations, even though you’ve actually gained $3,000. Conversely, if you were expecting no bonus and received $3,000, you would likely feel very pleased. This scenario shows how outcomes are evaluated relative to a reference point (in this case, the expected bonus), and deviations from this reference point are perceived as gains or losses.

3.9.4 Example of Diminishing Sensitivity

Scenario: Winning Lotteries

Consider two separate instances where: 1. Instance A: You win $100,000 in a lottery. 2. Instance B: Some time after, you win another $100,000 in a different lottery.

While both wins are undoubtedly positive, Prospect Theory suggests that the joy or utility you derive from the second $100,000 is less than that from the first $100,000. This is due to diminishing sensitivity; as your wealth increases, the additional satisfaction you get from each extra dollar decreases.

3.9.5 Example of Probability Weighting

Scenario: Rare Disease Prevention

Imagine you have two options to protect your town from a rare disease: 1. Option A: A vaccine that 100% prevents a disease that affects 1 out of 10,000 people. 2. Option B: A vaccine that 50% prevents a disease that affects 2 out of 10,000 people.

Even though both vaccines effectively have the same impact, people might disproportionately favor Option A, perceiving it as more beneficial because it completely eliminates the risk for the affected individual. This illustrates how people tend to overweight certain outcomes, even when the probabilistic expectations are equivalent.

3.10 Framing and its effect on decision making

3.10.1 Types of Framing

  • Risk Framing: Often used in scenarios involving risk and uncertainty, where the outcomes are presented in terms of potential gains or losses. For example, a medical treatment might be framed in terms of survival rates (positive framing) or mortality rates (negative framing).
  • Attribute Framing: This type affects how an attribute or feature of a situation or object is highlighted. For instance, beef labeled as “75% lean” tends to be more appealing than when it is labeled as “25% fat,” despite conveying the same information.
  • Goal Framing: This involves emphasizing the benefits of taking an action or the costs of not taking it. For example, a public service announcement might frame a message by highlighting the benefits of quitting smoking (positive frame) or the dangers of continuing the habit (negative frame).

3.10.2 Impact on Decision-Making

The framing effect impacts decision-making in various ways:

  1. Risk Behavior: People tend to become risk-averse when a decision is framed positively (focusing on gains) and more risk-seeking when the same decision is framed negatively (focusing on losses). This is closely related to loss aversion, where losses are felt more acutely than gains.

  2. Perception and Interpretation: Framing can alter one’s perception of the facts or the severity of a situation. For example, describing a glass as half full versus half empty can invoke different emotional reactions and judgments about the situation’s state.

  3. Choice and Preference: The way options are framed can lead to a change in preference among the same set of choices. This can be particularly notable in marketing, where product features might be highlighted in a way to influence consumer choice.

3.10.3 Mitigating the Framing Effect

  • Awareness of the framing effect is the first step in mitigating its influence on decision-making.
  • Critical thinking, seeking out information presented in different frames, and focusing on the factual content rather than its presentation can help individuals make more rational and informed decisions.
  • In professional settings, considering alternative framings of decisions and fostering an environment where diverse perspectives are encouraged can reduce the impact of framing on collective decision-making processes.

3.10.4 Summary

The framing effect illustrates the significant impact of presentation and context on decision-making, challenging the notion of human rationality in economic and psychological theories. By understanding and acknowledging the influence of framing, individuals and organizations can better navigate the complexities of decision-making, leading to more informed and balanced choices.

3.10.5 Examples - Framing and its effect on decision making

The framing effect can influence decision-making across a variety of contexts, from health care to finance, and even in our daily choices. Here are a few examples to illustrate how different frames can lead to different decisions or perceptions, even when the underlying information is identical.

Health Care Decisions

Example: A doctor explains the risks of an operation to two patients. To the first patient, the doctor says, “The survival rate of this surgery is 90%.” To the second patient, the doctor says, “There is a 10% mortality rate for this surgery.” Even though both statements convey the same information, the first patient is likely to perceive the surgery as less risky than the second patient because the information is framed positively, emphasizing the chances of survival rather than the risk of death. This difference in perception can affect the patient’s willingness to undergo the surgery.

Financial Investments

Example: Consider two investment options presented by a financial advisor. To one group of clients, the advisor describes Investment A as having a “95% chance of not losing any money.” To another group, the advisor describes Investment A as having a “5% chance of losing money.” Here, the first group is more likely to invest in Investment A due to the positive framing, even though both groups received the same information about the investment’s risk.

Environmental Policy Support

Example: An environmental campaign seeks to reduce plastic usage by highlighting the impact of plastic waste. To one audience, the campaign message says, “Join us, and help save 100 marine animals every year by reducing your plastic usage.” To another audience, the message says, “Every year, 100 marine animals die due to plastic waste. Your action can stop this.” Although the underlying message is the same, the first, positively framed message might inspire people by focusing on the positive impact of their actions, while the second, negatively framed message emphasizes the grim consequences of inaction.

Product Marketing

Example: A meat packaging company labels its product in two different ways. For one product line, the label reads “80% lean meat,” while another equivalent product line reads “contains 20% fat.” Consumers are more likely to choose the “80% lean” option over the “20% fat” option, despite them being the same product. The positive framing of “lean meat” is more appealing than focusing on the fat content.

Mitigation Strategies

Recognizing the power of framing can help individuals critically evaluate how information is presented to them and make more informed decisions. For instance, by considering both the positive and negative frames of a health care decision (“90% survival rate” vs. “10% mortality rate”), patients can make more balanced choices that reflect their values and preferences, rather than being swayed by emotional reactions to a particular frame.