Models of Decision-Making

Learning Objectives

By the end of this chapter, you will be able to:

• Explain how choice behavior is studied in laboratory settings with both animals and humans
• Describe the matching law and its significance in understanding decision-making
• Compare and contrast optimization theory and momentary maximization theory
• Distinguish between normative and descriptive models of decision-making
• Apply expected utility theory to decision-making scenarios
• Evaluate the strengths and limitations of various decision-making models

Introduction

Humans, living in the insulated world of the 21st century, don’t normally make individual decisions which by themselves lead to life or death. Though of course other factors need to be considered, who you are today is in many ways a product of all of the judgments and choices you have made in your lifetime. Collectively, judgments and choices together comprise decision-making. Some of the choices you have made have been “good” choices and some have been “bad” choices. To say a choice is “good” or “bad” often refers to whether the choice leads to better outcomes at the moment (going to a party) or in the long-run (staying home and studying).

However, animals living in a world of constant struggle for survival often do make choices that have an immediate effect on their lives. They need to be more concerned with surviving in the here and now rather than long-term planning. Consequently, the most important aspects of animal decision-making do not necessarily involve long-term planning, but issues of acquisition of food and mates right now. There is no cognitive ability that has a stronger evolutionary selective pressure than an ability to make a choice. The mechanisms of choice behavior seem to be very similar between animals and humans; therefore, keeping in line with the simple systems approach, we will begin our discussion with animal models of decision-making in order to better understand choice in humans, but quickly move on to human decision-making.

Studying Choice in the Laboratory

Procedures for Measuring Choice in Animals and Humans

Much research with animals discussed so far implemented the use of a single choice in a Skinner box and measures such as rate of responding or latency to respond have been recorded. These types of procedures were best exemplified by the FI, VI, FR, and VR schedules. However, if a Skinner box is designed with two or more response options, then the animal’s choices and preferences can be measured.

As an example, a pigeon can be presented with two keys in a Skinner box simultaneously, one red and one green. Pecking of the red key might lead to immediate access to a food hopper containing mixed grains for 2 s; pecking the green key might lead to a delay of 4 s followed by access to the food hopper for 4 s. These two options are often referred to as smaller-sooner (SS) and larger-later (LL). Researchers use this basic procedure to measure an animal’s ability to exhibit self-control.

Most animal research on choice is slow and tedious, only a single animal’s choices can be measured at any one time and it takes a long time to get an animal trained in the procedure used. But, as you might be able to guess by now, animal research takes advantage of the simple-systems approach and allows researchers to hold outside factors, such as hunger and motivation, constant.

Similar choice procedures can be used with humans (though I don’t encourage the use of an actual Skinner box with humans), two or more options can be presented and the researcher can make behavioral measures such as latency to choose or percent choices for one of the options. Walter Mischel did a series of fascinating studies with children measuring preferences for marshmallows and pretzels. However, measuring choice in humans can take advantage of the power of language, and people can simply be asked for their preferences. Instead of using mixed grains as with pigeons, a procedure to measure self-control could involve asking people to choose between options. A person might be asked, “which do you prefer, $5000 that you will receive today (SS) or $10,000 that you will receive in a year”.

Using hypothetical choices like this can lead to faster research which involves many more participants (hundreds or even thousands when we take advantage of the possibilities of the internet), allowing researchers to be more confident in their findings. However, in research with humans there are many more possible sources of error involved, such as motivation. If a person currently owes a loan-shark $5000, then they will almost inevitably choose the SS option in the previously described research, even if they normally would prefer the LL option.

The Matching Law as a Description of Choice Behavior

Herrnstein (1961) exposed pigeons to two different variable interval (VI) schedules simultaneously each on a separate key. If you recall, in a VI reinforcement schedule, only one response is required in order to receive reinforcement, but some amount of time must pass before a response is effective. In a VI 2 min schedule, though the exact duration between available reinforcers is not totally predictable on each trial, the durations will average 2 min though it might be 15 s on one trial and 10 min on another. However, if a pigeon pecked continually on a VI 2 min schedule, we would expect it to receive close to 30 reinforcers per hour. If given a VI 30 s schedule, we would expect the pigeon to receive about 120 reinforcers per hour.

Because there were two different reinforcement schedules available simultaneously in Herrnstein’s study, the pigeons’ choices could be measured. Herrnstein looked at many different VI schedule combinations and found several things. First, if the VI schedules were the same, the pigeons spent almost exactly as much time pecking one as the other. Second, if given the choice between two VI schedules that were different, they consistently preferred (spent more time pecking) the schedule associated with the higher rate of reinforcement. But most interestingly, as seen in Figure 1, the percentage of time spent on a schedule almost perfectly matched the reinforcement rate from that schedule.

More recent work by Grace, Bragason, & McLean (2003) showed that pigeons’ behaviors shift very rapidly toward this matching pattern, even when the schedules are continually changing. Herrnstein proposed the matching law to explain these results (Herrnstein, 1997). The matching law states that in a two-choice situation, the percentage of time spent on each alternative will match the percentage of reinforcers received from each alternative and is summarized by the following equation:

B₁/(B₁ + B₂) = R₁/(R₁ + R₂)

In this equation we see that the ratio of behaviors on alternative one (B₁) over the total behaviors in the two choice procedure (B₁ + B₂) will equal the ratio of reinforcers received from alternative one (R₁) over the total reinforcers received in the two choice procedure (R₁ + R₂). Don’t let the math scare you, all this means is that the pigeons in Herrnstein’s experiment matched effort to outcome (McDowell, 2005).

Think About It

The law of gravity is often considered to be a primary law because it seems to reveal something about the nature of the universe. Do you think that the Matching Law is a law in the same sense that animal behavior always follows it?

Of course there are exceptions to the matching law (Baum, 1974). Sometimes an animal expresses an indifference to the two available choices, regardless of the schedules, then they are displaying undermatching. If the animals consistently prefer one of the options, regardless of the schedules, then they are displaying bias (I had a pigeon once with a bias for red keys, it didn’t really matter what the key led to, it always pecked the red one). Suffice it to say, despite these exceptions, the matching law does a good job describing the behavior of animals and humans in many situations.

As an example, Conger and Killeen (1974) put together four members of a group and instructed them to discuss drug abuse. Three of the group members were confederates, working for the experimenters. One of the confederates had the job of keeping the conversation going. The other two confederates administered verbal “reinforcement” on VI schedules. The real participant directed the conversation toward these two confederates at a rate roughly matching the rates of reinforcement they were administering.

Theories to Explain Choice Behavior Arising from Laboratory Animals

Optimization Theory

Microeconomics studies the behavior of individual consumers as they attempt to acquire relatively scarce goods. Psychologists study operant conditioning which involves the behavior of individuals as they pursue reinforcers. If we substitute “relatively scarce goods” with “reinforcers” we find that these two methods of studying behavior are highly similar. Behavioral economics is the study of how organisms “spend” their limited resources, including time and money, it is essentially the study of how animals make choices.

The optimization theory of choice explains the matching law discovered by Herrnstein and argues that consumers will “spend” their resources in that way that will maximize their utility (Rachlin, Green, Kagel, & Battalio, 1976; Baum & Aparicio, 1999). Utility is a subjective measure used in economics to describe satisfaction or happiness. Essentially, utility arose in economic theory because everyone is different, not everyone’s satisfaction derives from money. If given the choice between $10 and a new book, some people choose the $10, some people choose the book. Presumably, those that choose the $10 receive more utility from $10 than they would receive from the book. For those that choose the book, they receive more utility than they would have if choosing the $10.

Of course, life is much more complicated than a single choice with a single outcome. If you receive a $2000 paycheck each month, you need to make some choices. Perhaps you’ll pay the bills, maybe donate some to the church, you might take a trip. Those things that you value will be different from what others value and others might spend their $2000 quite differently. Regardless of how you spend your $2000, optimization theory argues that you will maximize your utility.

If you decide to spend a weekend having a “movie marathon” you might go to the video store and choose 10 movies for your marathon. Since you enjoy both comedies and dramas, you need to choose how many of each you will rent. Perhaps you like dramas a little more than comedies, dramas might give you more satisfaction. However, like anything, your movie viewing is subject to the law of diminishing marginal value (economic law that each additional unit of a commodity produces less value to you). The first drama you watch might lead to 5 units of utility, whereas the second drama is slightly less satisfying (4½ units). If you were to rent ten dramas, the tenth one might only give you ½ unit of utility.

Though you prefer drama, it is the package of six dramas and four comedies that would give you the highest utility. This example seems far-fetched, after all, who has the time or ability to calculate all of that? Despite this, researchers have found repeatedly that animals and humans do in fact approach optimality in their choices.

Studies of choice behavior with animals don’t use movie rentals, but instead tend to focus on those things that are important to animals, namely mating and food (Krebs & Davies, 1978). The waiting time of male dung flies at cow patties provides an excellent example of optimization (for those of you not from farm country, “patties” are cow poop). A little background is in order, female dung flies lay eggs in cow patties and prefer fresh patties to old patties. Male dung-flies search for fresh patties, then wait for females. However, the longer they wait at a patty for a female, the less “fresh” the patty becomes. A fresher patty has a better chance of attracting a female, but finding a fresh patty takes time. The male dung-fly needs to make a choice about when to move to a fresh patty. Parker (1978) calculated the ideal wait time for male dung-flies based on density of patties in a field. The male dung-flies behaved as though they somehow had access to Parker’s calculations, they waited almost exactly as long as predicted.

Momentary Maximization Theory

Momentary maximization theory argues that organisms make choices based on which alternative is better at the moment (Shimp, 1966). Many factors will influence which alternative will lead to maximal utility at the moment including qualities of the reinforcer and deprivation of the organism. Though the momentary maximization theory and optimization theory often predict the same choices, they definitely don’t make the same predictions in all situations because the best choice in the short-run is not always the best choice in the long-run. We will return to the question of momentary maximization vs. optimization in our discussion of self-control choices.

Check Your Learning

• In many ways the lives we have are a product of the choices we have made.
• Studying choice behavior is relatively easy and often involves simply counting and recording behaviors.
• When studying choice in humans we can take advantage of the power of language and study about not just actual choices, but hypothetical choices as well.
• Research with animals and humans has discovered the matching law as a basic description of decision-making. The matching law basically states that animals will allocate their time in direct proportion to the amount of reinforcement they receive from different sources.
• Optimization theory provides a link between the study of choice and evolutionary theory by describing how organisms maximally benefit from the “optimal” choices that lead to the best long-term outcomes.
• Momentary maximization theory provides an explanation for decision-making that leads to the best outcome at the moment at the expense of long-term optimal solutions.

Theories of Choice Behavior Arising from Work with Humans

Normative Models of Decision-Making

Normative models of decision-making are those models of decision-making that describe the way people should make decisions. These models describe perfect decision-making by people when they have access to all information relevant to a decision, spend the time considering it all, and have the capacity to actually deal with all of the relevant information. In theory, normative models of decision-making lead to the “best” outcome every time, much like optimization theory previously described. The best option is always the one that leads to the most utility. Expected Utility Theory and Probability Theory are two of the more famous normative models of decision-making.

Expected Utility Theory, Subjective Expected Utility Theory and Utilitarianism

Expected utility theory states that when facing uncertainty, people should maximize their utility function (Baron, 2003). An example will explain this theory the best. Farmer Brown is thinking about planning crops this year, he knows some things about these crops. He knows that if the weather is perfect this year, then Crop A will yield 10 units of utility, Crop B will yield 8 units of utility, and Crop C will yield 3 units of utility. This example incorporates decision-making under certainty and is simple (Rubinstein, 1975; Rubinstein & Pfeiffer, 1980). If Farmer Brown knows the weather will be perfect this growing season he should plant Crop A, if the weather will be fair he should plant Crop B and if the weather will be poor, he should plant Crop C. He will maximize his utility in any of these situations. Of course, we rarely have access to all of the information needed to make the best decision.

It seems reasonable to conclude that Farmer Brown would have access to the yields of the different crops during different growing seasons, after all, Farmer Brown is not stupid, he knows the yields for the crops for other farmers from years past in different growing seasons. However, it seems unreasonable that Farmer Brown would “know” for certain what kind of growing season he was facing. Instead, Farmer Brown would probably consult his Old Farmer’s Almanac and make some guesses about the weather for the next year. Let’s pretend that the Almanac told Farmer Brown that there was a 15% chance that the weather this growing season would be perfect, a 60% chance the weather would be fair, and a 25% chance that the weather would be poor.

Farmer Brown would need to calculate the expected utility of each crop (for Crop A, 15%×10 + 60%×1 + 25%×-2 = 1.6) and then choose the crop with the highest expected utility. The calculation of expected utility is:

Expected Utility = Σ(probability × value)

Given these expectations for the growing season, Farmer Brown should plant Crop B because it maximizes his expected utility this year, but it does not guarantee him the maximum output this year. If the weather is poor this year, poor Farmer Brown is going to kick himself, but this is the risk he has to take to make the “right” choice. Consequently, this situation is known as decision-making under risk (Rubinstein, 1975). Unfortunately, in many situations, we do not have access to the probabilities of the different outcomes and we need to make some guesses. Decision-making under uncertainty is the most common way we are forced to make our choices and expected utility theory falls short.

Subjective expected utility theory was proposed because objective probabilities of outcomes or events cannot always be determined in advance and people need to use subjective probabilities, based on their own estimations (Slovic, Fischhoff, & Lichtenstein, 1977). When forced to make decisions under uncertainty, we can make use of a decision tree to help us maximize our outcomes. Let us suppose that a juror has been sitting in the jury box listening to testimony. The juror, of course, has two choices, to convict or acquit the defendant. After hearing all of the testimony, perhaps they are 60% certain the defendant is guilty (therefore leaving a 40% chance of innocence). The juror could lay out all of the possible outcomes (there are really four, convict/guilty, convict/innocent, acquit/guilty, and acquit/innocent). The juror might give values to these outcomes based on personal judgment. The juror might believe the best outcome is acquitting an innocent person and the worst outcome is convicting an innocent person and so these two outcomes are given values of +100 and -100. The juror then assigns values for the other possible outcomes that fall between these extremes.

Calculating the expected utility of each branch of the decision tree reveals that the expected utility of acquittal in this case is +10 whereas the expected utility of conviction in this case is -4. This juror, given their 60% certainty of guilt and these values for outcomes, should vote for acquittal. As a good demonstration of the generality of this theory, Shanteau and Nagy (1979) used subjective expected utility theory to predict which dating partner females would choose whereas Lynch and Cohen (1978) used subjective expected utility theory to predict helping behavior.

Think About It

Do you think that we should only consider consequences to ourselves when making choices? Shouldn’t we also consider how our choices affect other people?

Utilitarianism, first proposed in the modern sense by Jeremy Bentham and latched onto by many social reformers such as Karl Marx, is clearly a cousin of expected utility theory. It is a theory of morality that argues that a moral choice is one that allows us to maximize utility (Bentham, 1876). The difference emerges, however, when we realize that our actions affect not only ourselves, but others as well. Stated again, utilitarianism argues that moral decisions are those that maximize utility when considering all others that are affected by the outcomes of your decision. In our modern and global world, the effects of our decisions are felt by people we have never met and, according to utilitarianism, we need to consider the impact of our decisions on their utility.

This issue is coming to a head with our understanding of the effects of our behaviors on climate change where we are modifying the lives of people not only on the other side of the world, but we are modifying the lives of people that haven’t even been born yet (Ekins, 1999). Act utilitarianism argues that the individual act which would lead to the greatest good for the greatest number or the least pain for the least number should always be chosen. Rule utilitarianism argues that the best act is to follow a general rule which usually leads to the most utility. Though the differences between act and rule utilitarianism are usually little more than academic, these differences can lead to interesting discussion of moral decision-making (Emmons, 1973).

If a doctor has an otherwise healthy patient come in for an appendix removal, is the doctor justified in harvesting the organs (heart and two kidneys) of this otherwise healthy patient to give to three other patients that need these organs to survive. Act utilitarianism would say yes, harvest the organs, the lives of three individuals will be saved while the only one individual will be lost. Rule utilitarianism would argue that the doctor needs to follow the general rule that usually leads to the most utility, namely, doctors do not harm patients.

Probability Theory

Another normative model of decision-making you may already be somewhat familiar with is probability theory. Probability theory is a branch of mathematics that deals with random events and allows for the best “average” prediction to be made. Probability theory is most famously used by scientists in the form of statistics, which allow us to make decisions that are “probably” correct (Baron, 2003). Common decisions made using probability theory include the questions of whether two samples are significantly different from each other and whether or not to reject the null hypothesis. Though it is important to realize that probability theory is a normative model of decision-making, clearly the details of probability theory are far beyond this course. I will leave it to your statistics professor to explain this stuff for you!

Descriptive Models of Decision-Making

Descriptive models of decision-making are those models of decision-making that describe the way people actually do make decisions. Often, people do not have access to all of the relevant information pertaining to the decision to be made, or they are too lazy or busy to deal with it all, or they cannot handle it all. Even though descriptive models of decision-making often lead to options that are less than the best, the outcomes are usually “good enough”. Descriptive models of decision-making are cognitively cheap to utilize and we often the only types of decision-making we can use since we don’t really have access to all the relevant information anyway. Descriptive models of decision-making prevail in actual decision-making. Satisficing, Prospect Theory, and Regret Theory are three of the more common descriptive models of decision-making.

Satisficing

When I first started looking for a house, I had lots of goals. I wanted it to be close to my and my wife’s work. I wanted it to be in the best school district. I wanted it to have four bedrooms. I wanted it to be two-stories. I wanted it to be large. Oh yeah, don’t forget the price. When I started looking at houses I realized just how many there were in my urban area. Many of them had many of my desired attributes, but none had all of them. Since no house had every one of my desired properties, I needed to find a way to compare them. Ideally, I’d be able to quantify the utility each house would give me and then simply choose the one with the highest utility, perhaps through the use of a decision tree. I tried creating a spreadsheet and laying out the merits of each possible house, but it became readily obvious it wasn’t going to work. Is a three-bedroom house in a good school district better or worse than a four-bedroom house in a slightly worse school district?

Instead, I had to decide the satisfactory level for each desired attribute. I decided that even though there were some houses that were very close to both my and my wife’s work, as long as the house was within 30 min for both of us, it was good enough. I decided the house didn’t need to be in the best school district (exemplary is the term used), as long as it was good enough (recognized). I decided that four bedrooms was an attribute I would not budge on. I decided that though I wanted a two-story home, a one-story home would be “good enough”.

This type of decision-making exemplifies satisficing, which is an alternative to optimization proposed by Herbert Simon (1956) for cases where there are multiple and competitive objectives in which one gives up the idea of obtaining a “best” solution. When there are multiple objectives which compete with each other, the quantitative computation of the best outcome may not be possible. When satisficing, the decision maker sets minimum acceptable levels for the various objectives that would be “good enough” and then seeks a solution that will exceed these minimums. Admittedly, when satisficing it is definitely possible to miss out on a better choice, but given the limits of human cognitive capacity, we often need to settle for “good enough” (Schwartz et al., 2002; Diab, Gillespie, & Highhouse, 2008; Parker, de Bruin, & Fischoff, 2007).

Prospect Theory

Kahneman and Tversky (1979; 1984) noticed that expected utility theory was able to find the exact “right” solution every time (the one that maximized expected utility), however, they also noticed that people’s actual decisions often deviated from the ideal decisions. Prospect theory was proposed as an alternative to expected utility theory to describe how people make decisions when facing uncertainty. Kahneman was given the Nobel Prize in economics in 2002 for this work (unfortunately, Tversky died in 1996, otherwise it seems undoubted that he would have shared the award).

Prospect theory replaces the notion of utility with value which is figured in terms of gains and losses in relation to a reference point (Schwartz, Goldberg, & Hazen, 2008). The value function for gains is different from the value function for losses. The value function for gains tends to be somewhat shallow whereas the value function for losses tends to be relatively steep. The effect of this is that we tend to feel losses more heavily than gains. From a practical perspective, this means that though we might pay 50c for a candy bar, it might take 75c to convince us to sell it. This discrepancy is known as the mere-ownership effect (Reb & Connolly, 2007).

If a question is framed in terms of gains, people’s choices are different than if the exact same question is framed in terms of losses. This is because people tend to be risk-averse for gains, but risk-seeking for losses. Take the following as an example of risk-aversion for losses: “To illustrate risk aversion…, consider the choice between a prospect that offers an 85% chance to win $1000 (with a 15% chance to win nothing) and the alternative of receiving $800 for sure. A large majority of people prefer the sure thing over the gamble, although the gamble has higher (mathematical) expectation.” (Kahneman & Tversky, 1985, p. 341).

If this same gamble is framed in terms of losses, people tend to be risk-seeking: “Consider, for example, a situation in which an individual is forced to choose between an 85% chance to lose $1,000 (with a 15% chance to lose nothing) and a sure loss of $800. A large majority of people express a preference for the gamble over the sure loss. This is a risk seeking choice because the expectation of the gamble (—$850) is inferior to the expectation of the sure loss (—$800)” (p. 342). The differences in our choices comes simply from the shape of our value function.

Regret Theory

Another alternative that tries to explain deviations from the choices predicted by expected utility theory is regret theory (Loomes & Sugden, 1982). Just like prospect theory, regret theory explains why people tend to be risk-averse in some situations but risk-seeking in others. Regret theory assumes that people sometimes experience feelings of regret after making choices and these feelings of regret are undesirable. People take these feelings of regret into account when making choices and try to avoid them.

As described previously, people tend to display risk aversion for a positive gamble: when asked to choose either $800 for sure or an 85% chance at $1000 most people choose the $800 for sure. Regret theory argues that this choice is made in order to avoid the possible regret we would feel if we chose the probabilistic option and lost. People tend to be risk seeking when it comes to losses: when asked to choose between an 85% chance of a loss of $1000 or a sure loss of $800, people tend to choose the gamble. Regret theory argues we seek this risk to avoid the regret we would feel if we picked the sure loss, we would regret not taking the gamble because we could have lost $0 (Plous, 1993; Marcato & Ferrante, 2008).

Check Your Learning

• Theories of choice arising from work with humans can be categorized as either normative (explaining how we should decide) or descriptive (explaining how we actually do decide).
• Expected utility theory, arising from economics, is the decision-making strategy that leads to the best outcomes.
• Utilitarianism, an extension of expected utility theory, reminds us that we need to consider the consequences of our choices not only as they affect us, but as they affect others as well.
• In most decision-making situations there is a degree of probability and uncertainty.
• Because of uncertainty, humans seem to fall back on strategies such as satisficing and regret avoidance when making choices.

Key Terms

Act Utilitarianism: Approach to utilitarianism that argues that each time a decision is made, the act which leads to the greatest good for the greatest number should always be performed.

Behavioral Economics: The study of how organisms allocate their limited resources, including time and money.

Bias: A deviation from matching in which an animal consistently prefers one alternative in a two choice procedure, regardless of the outcomes of the alternatives.

Choice: A form of decision-making which involves selecting among competing alternatives.

Decision-Making: General term which incorporates choice among alternatives and judgment formation.

Decision-Making Under Certainty: Decisions made in which the factors that determine the outcomes of the different choice alternatives are known with certainty.

Decision-Making Under Risk: Decisions made in which the factors that determine the outcomes of the different choice alternatives occur with known probabilities.

Decision-Making Under Uncertainty: Decisions made in which the probabilities of the different factors which affect the outcomes of the different choices are uncertain.

Decision Tree: A graph used to guide decision-making which shows all possible choices and all possible outcomes.

Descriptive Models of Decision-Making: Those models of decision-making that address how humans actually make decisions. Include prospect theory, satisficing, and regret theory.

Expected Utility: Calculated by multiplying the probability of an outcome times the value of the outcome. With multiple possible outcomes, this calculation must be completed multiple times and the resulting products added together.

Expected Utility Theory: When facing uncertainty, people should behave as if they were maximizing the utility function of the possible outcomes.

Law of Diminishing Marginal Value: Economic law that each additional unit of a commodity produces less value to you.

Matching Law: In a two-choice situation, the percentage of time spent on each alternative will match the percentage of reinforcers received from each alternative.

Mere-Ownership Effect: The tendency to value items more highly simply because we own them.

Momentary Maximization Theory: Theory of choice that argues that organisms make choices to maximize their satisfaction (utility) at the present moment.

Normative Models of Decision-Making: Those models of decision-making that address how humans should make decisions. Include expected utility theory and probability theory.

Optimization Theory: Theory of choice behavior which assumes that consumers spend their resources in the way that maximizes their utility.

Probability Theory: A branch of mathematics that deals with random events and allows for the best “average” prediction to be made.

Prospect Theory: Descriptive theory of choice under risk. Developed to account for differences between the ideal perfect choices predicted by expected utility theory and the way people really make choices.

Regret Theory: Descriptive model of decision-making that assumes that decision makers experience feelings of regret and try to avoid this feeling of regret when making choices.

Rule Utilitarianism: An approach to utilitarianism that argues that rather than considering every decision separately, general rules should be followed which tend to lead to the greatest good for the greatest number.

Satisficing: Theory of decision-making that argues that in the face of uncertainty and incomplete information, decision makers work to satisfy basic needs, even if the choice does not optimize overall utility.

Smaller-Sooner (SS): A choice option that provides a smaller reward that is available immediately.

Larger-Later (LL): A choice option that provides a larger reward that is delayed in time.

Subjective Expected Utility Theory: An extension of expected utility theory which is used when a probability of an outcome or event cannot be determined in advance and subjective probabilities, determined by the decision maker, must be used.

Undermatching: A deviation from matching in which animals express relative indifference to the alternatives in a two choice procedure, regardless of the outcomes of each alternative.

Utilitarianism: Theory of morality that argues that the best choice is always the one which maximizes the utility of all people that will be affected.

Utility: Measure used in economics to describe the satisfaction one receives from a product or outcome. Because everyone is different, those things that create utility for one person may not create utility for others.

References

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