Choice Under Uncertainty

Lotteries

Assume we have $n$ states of the world and a probability distribution over the states. A lottery is a real-random variable. Or put simply,

Definition: A lottery is a list of prizes and probabilities,

$$\ell=\left((x_1,x_2,\ldots,x_n),(p_1,p_2,\ldots,p_n)\right),$$

where $x_k \in \mathbb{R}^m$ is the prize the lottery gives when state $k$ occurs, and $p_k\ge 0$ is the probability that state $k$ occurs.

Lotteries

In practice, it is very useful to depict lotteries as a graph.

$\ell$ $x_1$ $x_2$ $\ldots$ $x_n$ $p_1$ $p_2$ $p_n$

The Certain Lottery

The lottery that gives prize $x$ with probability one (with certainty) is denoted by: $$\delta_x=\left((x),(1)\right).$$

$\delta_x$ $x$ $1$

Expectation and Variance

The expected value of a lottery $\ell_1=\left((x_1,x_2,\ldots,x_n),(p_1,p_2,\ldots,p_n)\right)$ is: $$\mathbb{E}[\ell_1]=p_1\cdot x_1+p_2\cdot x_2 +\ldots p_n\cdot x_n = \sum_{i=1}^n p_i\cdot x_i;$$ and the variance of a lottery $\ell$ is: $$\begin{aligned}\mathrm{Var}[\ell_1]=&p_1\cdot (x_1-\mathbb{E}[\ell_1])^2+p_2\cdot (x_2-\mathbb{E}[\ell_1])^2 +\ldots p_n\cdot (x_n-\mathbb{E}[\ell_1])^2 = \\ =& \sum_{i=1}^n p_i\cdot (x_i-\mathbb{E}[\ell_1])^2.\end{aligned}$$

Composition of Lotteries

Given two lotteries, $\ell_1=\left((x_1,x_2,\ldots,x_n),(p_1,p_2,\ldots,p_n)\right)$ and $\ell_2=\left((y_1,y_2,\ldots,y_m),(q_1,q_2,\ldots,q_n)\right)$ and a number $\alpha$ in the interval $(0,1)$, we can create a compound lottery $\ell$ that plays $\ell_1$ with prob. $\alpha$ and $\ell_2$ with prob. $1-\alpha$.

The compound lottery $\ell$ plays $\ell_1$ with prob. $\alpha$ and $\ell_2$ with prob. $1-\alpha$:

$\ell$ $\ell_1$ $\ell_2$ $x_1$ $x_2$ $y_1$ $y_2$ $\alpha$ $1-\alpha$ $p$ $1-p$ $q$ $1-q$

$$\ell = \alpha\ell_1 \oplus (1-\alpha)\ell_2 = \left((x_1,x_2,y_1,y_2),(\alpha p,\,\alpha(1-p),\,(1-\alpha)q,\,(1-\alpha)(1-q))\right)$$

The reduced form — $\ell$ connects directly to prizes:

$\ell$ $x_1$ $x_2$ $y_1$ $y_2$ $\alpha \cdot p$ $\alpha \cdot (1-p)$ $(1-\alpha) \cdot q$ $(1-\alpha) \cdot (1-q)$

$$\ell = \alpha\ell_1 \oplus (1-\alpha)\ell_2 = \left((x_1,x_2,y_1,y_2),(\alpha p,\,\alpha(1-p),\,(1-\alpha)q,\,(1-\alpha)(1-q))\right)$$

Preferences Over Lotteries

Given two lotteries $\ell_a$ and $\ell_b$ such that a decision maker (DM) chooses $\ell_a$ over $\ell_b$,
the following statements are equivalent:

For simplicity we write:

  • $\ell_a\succ \ell_b$ when $\ell_a\succeq\ell_b$ but $\ell_b\not\succeq\ell_a$ (strict preference)
  • $\ell_a\sim \ell_b$ when $\ell_a\succeq\ell_b$ and $\ell_b\succeq\ell_a$ (indifference).

Preferences Over Lotteries

von Neumann & Morgenstern's Assumptions

Completeness
For any two lotteries $\ell_1$ and $\ell_2$,
$\ell_1\succeq \ell_2$ and/or $\ell_2\succeq \ell_1$.
Transitivity
For any lotteries $\ell_1$, $\ell_2$ and $\ell_3$,
if $\ell_1\succeq \ell_2$ and $\ell_2\succeq \ell_3$ then $\ell_1\succeq \ell_3$.
Continuity
If $\ell_1\succeq\ell_2\succeq \ell_3$ then exists $p\in[0,1]$ such that $\ell_2\sim p\ell_1\oplus(1-p)\ell_3$.
Independence
If $\ell_1\succ\ell_2$ then for any $\ell_3$ and any $0<p<1$, $p\ell_1\oplus (1-p)\ell_3\succ p\ell_2\oplus(1-p)\ell_3$.

If $\succeq$ satisfy all of the above, there exists $u:\mathbb{R}\rightarrow\mathbb{R}$ such that $$\left((x_1,\ldots,x_n),(p_1,\ldots,p_n)\right)\succ \left((y_1,\ldots,y_m),(q_1,\ldots,q_m)\right)$$ if and only if $$\sum_{k=1}^n u(x_k)\cdot p_k> \sum_{k=1}^m u(y_k)\cdot q_k.$$

Expected Utility

We write: $$ U(\ell_1)=u(x_1)\cdot p_1+\ldots + u(x_n)\cdot p_n, $$

and refer to $U$ as the expected utility

and to $u$ as the:

  1. utility for money or
  2. Bernoulli utility or
  3. von Neumann-Morgenstern utility (vNM)

Risk Aversion

$$u(x)=2\frac{x^{\alpha}-100^{\alpha}}{102^{\alpha}-100^{\alpha}}+100$$
$$X=\begin{cases}100 & \text{with probability } 1/2\\ 102 & \text{with probability } 1/2\end{cases}$$
x u 100 101 102 $u(100)$ $u(102)$ $U(X)\!=\!{\scriptsize\tfrac{u(100)}{2}+\tfrac{u(102)}{2}}$ $u(101)$
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Risk Aversion

Consider two lotteries $A=(x_0,1)$ and $B=\left((x_0+M,x_0-M),\left(\frac{1}{2},\frac{1}{2}\right)\right)$ with $M>0$. That is, $A$ is a sure lottery (no risk) and $B$ is a risky lottery in which you win or lose $M$ with equal probabilities.

Notice both lotteries have the identical expected value, $\E[A]=\E[B]=x_0$.

The difference in expected utility of these lotteries is approximately,

$$U(B)-U(A)=\frac{M}{2}\cdot\left(Mu(x_0+M)-Mu(x_0-M)\right)$$

Expected Utility Theory

Attitudes Towards Risk

  1. Diminishing marginal utility, $u$ is concave, $u^{\prime\prime}<0\Rightarrow$, the consumer is risk-averse. $$U(X)<u(\mathrm{E}[X]) \text{ for all } X$$
  2. Increasing marginal utility, $u$ is convex, $u^{\prime\prime}>0\Rightarrow$, the consumer is risk-loving. $$U(X)>u(\mathrm{E}[X]) \text{ for all } X$$
  3. Constant marginal utility, $u$ is affine (linear plus a constant), $u^{\prime\prime}=0\Rightarrow$, the consumer is risk-neutral, $$U(X)=u(\mathrm{E}[X]) \text{ for all } X$$

Measuring the Degree of Risk-Aversion

The Arrow-Pratt or Absolute Measure of Risk Aversion

Definition
The Arrow-Pratt absolute measure of risk-aversion of an agent with VN-M utility $u$ at wealth level $w$ is: $$\rho_{u}(w)=\dfrac{-u^{\prime\prime}(w)}{u^{\prime}(w)}.$$

If for two individuals with VN-M utilities $u$ and $\widetilde{u}$ we have that $\rho_{u}(w)>\rho_{\widetilde{u}}(w)$ for all wealth levels $w$ then we say that the agent with utility $u$ is more risk-averse than the agent with utility $\widetilde{u}$.

Measuring the Degree of Risk-Aversion

The Relative Measure of Risk-Aversion

Definition
The relative measure of risk-aversion of an agent with utility $u$ at wealth level $w$ is: $$r_{u}(w)=\dfrac{-u^{\prime\prime}(w)\,w}{u^{\prime}(w)}.$$

Risk attitudes may depend on the wealth level

Buying insurance and lottery tickets
“Offhand it appears inconsistent for the same person both to buy insurance and to gamble: he is willing to pay a premium, in the one case, to avoid risk, in the other, to bear risk.” Friedman & Savage (1948): “The Utility Analysis of Choices Involving Risk,” Journal of Political Economy, p. 286.
Buying insurance and lottery tickets

The utility of wealth drawn on the back of the block has decreasing marginal utility below $x=100$ and increasing marginal utility above it: $$u(x)=\begin{cases}10000-(100-x)^{2} & \text{if } x\le 100\\[2pt] 10000+(x-100)^{2} & \text{if } x\ge 100\end{cases}$$

Lottery A: a gamble over low wealth levels

$$A=\begin{cases}0 & \text{with probability } 1/2\\ 100 & \text{with probability } 1/2\end{cases}\qquad E_A=\tfrac{1}{2}\cdot 0+\tfrac{1}{2}\cdot 100=50$$

$$\begin{aligned}U(A)&=\tfrac{1}{2}\,u(0)+\tfrac{1}{2}\,u(100)=\tfrac{1}{2}\cdot 0+\tfrac{1}{2}\cdot 10000=5000\\[2pt] u(E_A)&=u(50)=7500\end{aligned}$$ So $U(A)\lt u(E_A)$: the chord lies below the curve. Over this range the individual is risk-averse and would pay to replace the lottery by its expected value.

Lottery B: a gamble over high wealth levels

$$B=\begin{cases}100 & \text{with probability } 1/2\\ 200 & \text{with probability } 1/2\end{cases}\qquad E_B=\tfrac{1}{2}\cdot 100+\tfrac{1}{2}\cdot 200=150$$

$$\begin{aligned}U(B)&=\tfrac{1}{2}\,u(100)+\tfrac{1}{2}\,u(200)=\tfrac{1}{2}\cdot 10000+\tfrac{1}{2}\cdot 20000=15000\\[2pt] u(E_B)&=u(150)=12500\end{aligned}$$ So $U(B)\gt u(E_B)$: the chord lies above the curve. Over this range the same individual is risk-loving and would pay to take the lottery instead of its expected value.

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