# An Introduction to Prospect Theory

Part Of: [Neuroeconomics] sequence
Content Summary: 1500 words, 15 min reading time

Preliminaries

Decisions are bridges between perception and action. Not all decisions are cognitive. Instead, they occur at all levels of the abstraction hierarchy, and include things like reflexes.

Theories of decision tend to constrain themselves to cognitive phenomena. They come in two flavors: descriptive (“how does it happen”) and normative (“how should it happen”).

Decision making often occur in the context of imperfect knowledge. We may use probability theory as a language to reason about uncertainty.

Let risk denote variance in the probability distribution of possible outcomes. Risk can exist regardless of whether a potential loss is involved. For example, a prospect that offers a 50-50 chance of paying $100 or nothing is more risky than a prospect that offers$50 for sure – even though the risky prospect entails no possibility of losing money.

Today, we will explore the history of decision theory, and the emergence of prospect theory. As the cornerstone of behavioral economics, prospect theory provides an important theoretical surface to the emerging discipline of neuroeconomics.

Maximizing Profit with Expected Value

Decision theories date back to the 17th century, and a correspondence between Pascal and Fermat. There, consumers were expected to maximize expected value (EV), which is defined as probability p multiplied by outcome value x.

$EV = px$

To illustrate, consider the following lottery tickets:

Suppose each ticket costs 50 cents, and you have one million dollars to spend. Crucially, it doesn’t matter which ticket you buy! Each of these tickets have the same expected value: $1. Thus, it doesn’t matter if you spend the million dollars on A, B, or C – each leads to the same amount of profit. The above tickets have equal expected value, but they do not have equal risk. We call people who prefer choice A risk averse; whereas someone who prefers C is risk seeking. Introducing Expected Utility Economic transactions can be difficult to evaluate. When trading an apple for an orange, which is more valuable? That depends on a person’s unique tastes. In other words, value is subjective. Let utility represent subjective value. We can treat utility as a function u() that operates on objective outcome x. Expected utility, then, is highly analogous to expected value: $EU = pu(x)$ Most economists treat utility functions as abstractions: people act as if motivated by a utility function. Neuroeconomic research, however, suggests that utility functions are physically constructed by the brain. Every person’s utility function may be different. If a person’s utility curve is linear, then expected utility converges onto expected value: $EU \rightarrow EV \mid u(x) = x$ Recall in the above lottery, the behavioral distinction between risk-seeking (preferring ticket A) and risk-averse (preferring C). Well, in practice most people prefer A. Why? We can explain this behave by appealing to the shape of the utility curve! Utility convexity produces risk aversion: In the above, we see the first$50 (first vertical line) produces more utility (first horizontal line) than the second $50. Intuitively, the first$50 is needed more than the second $50. The larger your wealth, the less your need. This phenomenon is known as diminishing marginal returns. Neoclassical Economics In 1947, von Neumann and Morgenstern formulated a set of axioms that are both necessary and sufficient for representing a decision-maker’s choices by the maximization of expected utility. Specifically, if you assume an agent’s preference set accomodates these axioms… 1. Completeness. People have preferences over all lotteries. $\forall L_1, L_2 \in L$ either $L_1 \leq L_2$ or $L_1 \geq L_1$ or $L_1 = L_2$ 2. Transitivity. Preferences are expressed consistently. $\forall L_1, L_2, L_3 \in L$ if $L_1 \leq L_2$ and $L_1 \leq L_2$ then $L_1 \leq L_3$ 3. Continuity. Preferences are expressed as probabilities. $L_1, L_2, L_3 \in L$ then $\exists \alpha, B$ s.t. $L_1 \geq L_2 \geq L_3$ iff $\alpha L_1 + (1-\alpha)L_3 \geq L_2 \geq BL_1 + (1 - B)L_3$ 4. Independence of Irrelevant Alternatives (IIA). Binary preferences don’t change by injecting a third lottery. … then those preferences always maximize expected utility. $L_1 \geq L_2$ iff $sum(p_1u(x_1) \geq p_2u(x_2)$ The above axioms constitute expected utility theory, and form the cornerstone for neoclassical economics. Expected utility theory bills itself as both a normative and descriptive theory: that we understand human decision making, and have a language to explain why it is correct. Challenges To Independence Axiom In the 1970s, expected utility theory came under heavy fire for failing to predict human behavior. The emerging school of behavioral economics gathered empirical evidence that Neumann-Morgenstern axioms were routinely violated in practice, especially the Independence Axiom (IIA). For example, the Allais paradox asks our preferences for the following choices: Most people prefer A (“certain win”) and D (“bigger number”). But these preferences are inconsistent, because C = 0.01A and D = 0.01B. The independence axiom instead predicts that A ≽ B if and only if C ≽ D. The Decoy effect is best illustrated with popcorn: Towards a Value Function Concurrently to these criticisms of the independence axiom, the heuristics and biases literature (led by Kahneman and Tversky) began to discover new behaviors that demanded explanation: • Risk Aversion. In most decisions, people tend to prefer smaller variance in outcomes. • Everyone prefers gains over losses, of course. Loss Aversion reflects that losses are felt more intensely than gains of equal magnitude. • The Endowment Effect. Things you own are intrinsically valued more highly. Framing decisions as gains or as losses affects choice behavior. Each of these behavioral findings violate the Independence Axiom (IIA), and cumulatively demanded a new theory. And in 1979, Kahneman and Tversky put forward prospect theory to explain all of the above effects. Their biggest innovation was to rethink the utility function. Do you recall how neoclassical economics appealed to $u(x)$ convexity to explain risk aversion? Prospect theory takes this approach yet further, and seeks to explain all of the above behaviors using a more complex shape of the utility function. Let value function $\textbf{v(x)}$ represent our updated notion of utility. We can define expected prospect $\textbf{EP}$ of a function as probability multiplied by the value function $EP = pv(x)$ Terminology aside, each theory only differs in the shape of its outcome function. Let us now look closer at the the shape of $v(x)$: This shape allows us to explain the above behaviors: The endowment effect captures the fact that we value things we own more highly. The reference point in $v(x)$, where $x = 0$, captures the status quo. Thus, the reference point allows us to differentiate gains and losses, thereby producing the endowment effect. Loss aversion captures the fact that losses are felt more strongly than gains. The magnitude of $v(x)$ is larger in the losses dimension. This asymmetry explains loss aversion. We have already explained risk aversion by concavity of the utility function $u(x)$. $v(x)$ retains convexity for material gains. Thus, we have retained our ability to explain risk aversion in situations of possible gains. For losses, $v(x)$ concavity predicts risk seeking. Towards a Weight Function Another behavioral discovery, however, immediately put prospect theory in doubt: • The Fourfold Pattern. For situations that involve very high or very low probabilities, participants often switch their approaches to risk. To be specific, here are the four situations and their resultant behaviors: 1. Fear of Disappointment. With a 95% chance to win$100, most people are risk averse.
2. Hope To Avoid Loss. With a 95% chance to lose $100, most people are risk seeking. 3. Hope Of Large Gain. With a 5% chance to win$100, most people are risk seeking.
4. Fear of Large Loss. With a 5% chance to lose \$100, most people are risk averse.

Crucially, $v(x)$ fails to predict this behavior. As we saw in the previous section, it predicts risk aversion for gains, and risk seeking for losses:

Failed predictions are not a death knell to a theory. Under certain conditions, they can inspire a theory to become stronger!

Prospect theory was improved by incorporating a more flexible weight function.

$EP = pv(x) \rightarrow EP = w(p)v(x)$

Where $w(p)$ has the following shape:

These are in fact two weight functions:

1. Explicit weights represent probabilities learned through language; e.g., when reading the sentence “there is a 5% chance of reward”.
2. Implicit weights represent probabilities learned through experience, e.g., when the last 5 out of 100 trials yielded a reward.

This change adds some mathematical muscle to the ancient proverb:

Humans don’t handle extreme probabilities well.

And indeed, the explicit weight function successfully recovers the fourfold pattern:

Takeaways

Today we have reviewed theories of expected value, expected utility (neoclassical economics), and prospect theory. Each theory corresponds to a particular set of conceptual commitments, as well a particular formula:

$EV = px$

$EU = pu(x)$

$EP = w(p)v(x)$

However, we can unify these into a single value formula V:

$V = w(p)v(x)$

In this light, EV and EU have the same structure as prospect theory. Prospect theory distinguishes itself by using empirically motivated shapes:

With these tools, prospect theory successfully recovers a wide swathe of economic behaviors.

Until next time.

# Glimcher: Neuroeconomic Analysis 2: Because vs. As-If

The life blood of the theoretician is constraint.

To understand why, one must look to the size of conceptspace. The cardinality of conceptspace is formidable. For every fact, how can there be only a countable number of explanations for that fact? How many theories of physics have managed to explain what it means for an apple to fall from a tree? Putting on our Church-Turing goggles, we know that every event can be at least approximated by a string of binary code, that represents that data.
The number of programs that can be fed to a Turing Machine to generate that particular string is unbounded.

Constraint is how theoreticians slice away ambiguity, how they localize truth in conceptspace. To say “no”, to say “that is not possible”, is a creative and generative act. Constraint is a goal, not a limitation.

After summarizing each of the three fields he seeks to link, Glimcher spends an entire chapter responding to a particular claim of Milton Friedman, which permeates the economic climate of modernity. Friedman argued that it is enough for economics to model behavior *as if* it is congruent to some specified mathematical structure. In his words:

“Now of course businessmen do not actually solve the system of simultaneous equations in terms of which the mathematical economist finds it convenient to express this hypothesis… The billiard player, if asked how he decides where to hit the ball, may say that he “just figures it out” then also rubs a rabbit’s foot just to make sure… the one statement is about as helpful as the other, and neither is a relevant test of the associated hypothesis”.

This, Glimcher argues, is precisely the wrong way to go about economics, and scientific inquiry in general. Because human beings are embodied, there exist physical, causal mechanisms that generate their economic behavior. To turn attention away from causal models is to throw away an enormously useful source of constraint. It is time for economics to move towards a Because Model, a Hard model, that is willing to speak of the causal web.

Despite this strong critique of economic neo-classicism, Glimcher is unwilling to abandon its traditions in favor of the emerging school of behavioral economics. Glimcher insists that the logical starting place of neoclassicism – the concise set of axioms – retains its fecundity. Instead, he calls for a causal revolution within theories such as expected utility; from Soft-EU to Hard-EU.

According to Glimcher, “the central linking hypothesis” of neuroeconomics is that, when choice behavior conforms to the neoclassical axioms (GARP), then the new neurological natural kind of the Subjective Value must obey the constraints imposed by GARP. Restated, within its predictive domain, classical economics constrain neuroscience because utility IS a neural encoding known as a subjective value.

Glimcher then goes on to establish more linkages, which he uses to constrain the data deluge currently experienced within neuroscience. Simultaneously, he employs known impossibilities of the human nervous system to close the door on physically non-realizable economic models. It is to neuroscience that we turn next.