lesson 4- uncertainty and behavorial econ Flashcards
uncertainty
The state of not knowing what will happen in the future; multiple possible outcomes. Example: Investing in the stock market.
risk
The possibility of experiencing a loss or negative outcome. Example: The risk of losing money in a stock market investment.
probability
The likelihood of a particular outcome occurring, expressed as a number between 0 and 1 (or as a percentage).
Expected Value (EV)
The average outcome you expect from a decision, considering the probabilities of different outcomes.
Formula: E(X) = Pr₁X₁ + Pr₂X₂ (two outcomes) or
E(X) = Pr₁X₁ + Pr₂X₂ + … + PrₙXₙ (n outcomes), where Prᵢ is the probability of outcome Xᵢ.
Example: A lottery with a 50% chance of winning $10 and a 50% chance of winning $20 has an EV of $15.
Risk Premium.
The amount of money a risk-averse person would pay to avoid a risky situation.
Formula: Risk premium = Expected money value – Certainty equivalent.
Certainty Equivalent.
The certain amount of money that an individual considers equally desirable as a risky asset.
Risk Aversion.
Preference for a certain outcome over a risky one with the same expected value. Visual aid: Draw a concave utility function.
- risk averse person had diminishing MU
- most common attitude towards risk
- utility of certain income > E.U of uncertain income
Risk Neutrality
Indifference between a certain outcome and a risky one with the same expected value. Visual aid: Draw a linear utility function.
- slope doesnt change
- MU is constant for risk neutral person
Risk Loving
Preference for a risky outcome over a certain one, even if the risky outcome has a lower expected value. Visual aid: Draw a convex utility function.
- MU of money is increasing
Standard Deviation (SD)
A measure of the variability or dispersion of a set of values around their mean (expected value). Note: A higher SD indicates higher risk.
Formula for Standard Deviation (Simple Example with two equal probability outcomes)
√[0.5 * (X₁ - EV)² + 0.5 * (X₂ - EV)²] where X1 and X2 are the outcomes and EV is the expected value.
Expected Utility (EU)
The average utility you expect from a decision, considering the probabilities of different outcomes and the utility associated with each outcome. (SUM OF UTILITIES ASSOCIATED WITH ALL POSSIBLE OUTCOMES)
Formula: EU(X) = Σᵢ₌₁ᴺ U(Xᵢ)Prᵢ.
Note: Utility is not necessarily linear with monetary value.
IN EV -> TAKE WEIGHTED AVERAGE OF VALUES
IN EU -> TAKE WEIGHTED AVERAGE OF UTILITY FUNCTION
REDUCING RISK - diversification and insurance
Diversification- Spreading investments across different assets to reduce overall risk. Example: Investing in stocks, bonds, and real estate.
Insurance- A mechanism to transfer risk from an individual to an insurance company. Example: Health insurance, car insurance.
Law of Large Numbers.- The average outcome of many similar events tends to approach the expected value as the number of events increases.
it enables insurance companies to provide insurance for which the premiums paid equals the expected value of losses being insured against.
A. Behavioral Economics
Homo Economicus
The traditional economic model that assumes perfectly rational individuals who maximize utility. Note: Behavioral economics challenges this assumption.
- The field of economics that studies how psychological factors influence economic decisions. Note: It acknowledges that people don’t always behave rationally.
Subjective Probability Weighting
The tendency to overestimate or underestimate the probabilities of certain outcomes, based on personal perception and bias.
Certainty Effect.
A reduction in winning chance is always a displeasure. But, we feel, a reduction
results in larger psychological effect when it is a reduction from certainty than
from uncertainty.
reduction of prob frm 100 to 90 is worse than reduction frm 90 to 80.
reference point
a benchmark against which we compare our options, significantly influencing our decisions
status quo bias
also called endowment effect - The tendency to value something more highly once we own it. Example: The mug experiment.
- emotional bias
- The preference for maintaining the current state of affairs. Example: Keeping an old phone even if there are better options available.
“Opt-Out” vs. “Opt-In” Policies.
Explain how default options influence choices, leading to higher participation rates in “opt-out” systems (organ donation example).
Opt-in systems: Individuals must actively choose to become organ donors. This requires an affirmative action, which many people may not take, even if they support organ donation in principle. Inertia, the tendency to do nothing, is at play.
Opt-out systems: Individuals are automatically enrolled as organ donors unless they explicitly choose to opt out. This leverages the status quo bias; the default is to donate, and many people will remain in that default state due to the effort involved in opting out. Even if they initially weren’t strongly committed to organ donation, the ease of remaining in the default state increases participation.
Loss Aversion
The pain from a loss is felt more strongly than the pleasure from an equivalent gain. The pain from a loss and the
pleasure from the same size of
gain is not symmetric.
Visual Aid: Illustrate with a graph showing steeper curve for losses, than for gains
Framing Effects.
How the presentation of information influences decisions.
relying on the context in which a choice is described when making a decision
Example: Skin cream packaging example.
Anchoring Effects.
The tendency to rely too heavily on the first piece of information received (the “anchor”) when making decisions.
Example: Charity donation example.
- The price of a painting sold at an art auction and the experts’ pre-sale
valuations are anchored on the price at which the painting previously
sold at auction.
Nudging.
Gently guiding people toward making better decisions without restricting their choices.
Behavioral findings of human nature
and psychology can manipulate
people’s decisions.
Example: The “Save More Tomorrow” plan for retirement savings, SP Group energy usage prompts.
Formulas
Expected Value (with two possible outcomes): E(X) = Pr₁X₁ + Pr₂X₂
Expected Value (with n possible outcomes): E(X) = Pr₁X₁ + Pr₂X₂ + … + PrₙXₙ
Expected Utility: EU(X) = Σᵢ₌₁ᴺ U(Xᵢ)Prᵢ (where the summation is from i=1 to N)
risk premium = expected value - certainty equivalent
