Issues of Measurability
The above example of classifying facial photographs into different categories of emotions is an example of a qualitative classification of the basic observations. Although qualitative categories and qualitative variables are perfectly valid in the physical, biological, and social sciences,
theories and hypotheses are most powerful when they involve quantitative independent and dependent variables. Many economists are of the opinion that economics has a more impressive scientific track record than anthropology because economists work with numerical
variables such as prices, quantities, and income, rather than with qualitative variables like trust, group identification, or loyalty. Most theories and hypotheses involving quantitative independent and dependent
variables are easier to test, to fine tune, and if necessary, to revise, than most theories and hypotheses involving qualitative variables.
Is uncertainty an inherently qualitative or quantitative construct? In the following sections we shall see that one of the two primary methods
of representing uncertainty—the so-called “objective approach”— represents uncertainty quantitatively, via numerical probabilities. On the other hand, the other primary method—the so-called “subjective
22 Machina
approach”—has traditionally represented uncertainty in a qualitative manner, via an unstructured set of states of nature. However, in the final section of this paper, we see that taking a measurable, quantitative approach to subjective uncertainty can enhance its power, and in many senses can serve as an almost complete substitute for what may be considered
the more ad hoc assumptions made about the world in the objective approach.
25 Aralık 2008 Perşembe
Issues of Classification
Issues of Classification
In order for a variable or phenomenon to satisfy the criterion of “scientific observability,” it is not enough that more than one scientist be able to see it—it is not even enough that a camera be able to record it. Rather, a variable is only scientifically observable if independent observers can agree on their description of what they have just observed. Thus, while a scientist can photograph facial expressions, they cannot be said to have photographed expressions of emotion unless there is a well-defined specification of which expressions correspond
to each emotion, and independent observers predominantly agree in their assignment of emotions to each photograph. In other words, scientific observability requires well-defined and commonly accepted classification schemes for the observations, sufficient for grouping and comparing such observations, and relating them to general
hypotheses and theories.
Just as different types of variables can have different degrees of observability, different classification schemes will have different degrees of common agreement. Thus, in regular consumer theory, we
States of the World and the State of Decision Theory 21
are much more prone to classify commodities and define preferences in terms of category schemes like {“fruits,” “vegetables,” “grains”} compared
to schemes like {“delicious foods,” “filling foods,” “unpleasant foods”}. Although the latter scheme is in some sense much more directly connected to any given individual’s preferences than the former scheme, the latter scheme cannot be defined independently of the particular consumer being studied. Since foods cannot be classified according to this latter scheme prior to observation of the consumer’s (verbal or choice) behavior, it cannot be used as a classification scheme for independent variables. Categories like “delicious foods,” “unpleasant
foods,” etc. can be defined for dependent variables, however, either on the basis of the consumer’s verbal expressions, or on the basis of their past purchases or consumption behavior. Thus, whether a given classification scheme does or does not satisfy the criterion of scientific observability may well depend upon whether the scheme is intended to be applied to the independent variables or to the dependent variables of a theory.
In order for a variable or phenomenon to satisfy the criterion of “scientific observability,” it is not enough that more than one scientist be able to see it—it is not even enough that a camera be able to record it. Rather, a variable is only scientifically observable if independent observers can agree on their description of what they have just observed. Thus, while a scientist can photograph facial expressions, they cannot be said to have photographed expressions of emotion unless there is a well-defined specification of which expressions correspond
to each emotion, and independent observers predominantly agree in their assignment of emotions to each photograph. In other words, scientific observability requires well-defined and commonly accepted classification schemes for the observations, sufficient for grouping and comparing such observations, and relating them to general
hypotheses and theories.
Just as different types of variables can have different degrees of observability, different classification schemes will have different degrees of common agreement. Thus, in regular consumer theory, we
States of the World and the State of Decision Theory 21
are much more prone to classify commodities and define preferences in terms of category schemes like {“fruits,” “vegetables,” “grains”} compared
to schemes like {“delicious foods,” “filling foods,” “unpleasant foods”}. Although the latter scheme is in some sense much more directly connected to any given individual’s preferences than the former scheme, the latter scheme cannot be defined independently of the particular consumer being studied. Since foods cannot be classified according to this latter scheme prior to observation of the consumer’s (verbal or choice) behavior, it cannot be used as a classification scheme for independent variables. Categories like “delicious foods,” “unpleasant
foods,” etc. can be defined for dependent variables, however, either on the basis of the consumer’s verbal expressions, or on the basis of their past purchases or consumption behavior. Thus, whether a given classification scheme does or does not satisfy the criterion of scientific observability may well depend upon whether the scheme is intended to be applied to the independent variables or to the dependent variables of a theory.
Issues of Observability
Issues of Observability
Because decision scientists cannot perform dissection, they are subject to a greater scientific discipline than that required of anatomists.
If a decision scientist tried to account for an individual’s purchases
of bananas as the direct result of something like an “appetite for fruit,” we would not know how to test this hypothesis—that is, we would not know how to independently “look for” such an appetite, even if we had a scalpel and an open, anesthetized brain. Such unob20
Machina
servable constructs like appetites, utility, and preferences can—and do—play a role in scientific decision theory, but only as inside links in a causal chain that ultimately starts with fully observable independent variables and ultimately ends with fully observable dependent variables.
For example, given the joint hypothesis that well-defined commodity
preferences exist and are also stable from day to day, standard consumer theory allows us to infer enough information about these preferences from an individual’s past demand behavior to be able to make refutable predictions about their future demand behavior, even for some combinations of prices and income never before observed.
In the following sections, we shall see that in passing from choice over certain commodity bundles to choice over uncertain prospects (either “objective lotteries” or “subjective acts”), hypotheses involving the unobservable constructs of commodity preferences and utility functions
can be replaced by hypotheses involving the unobservable constructs
of risk preferences and beliefs, which also link observable independent to observable dependent variables. Whether the notion of “states of nature” can similarly serve remains to be discussed.
Because decision scientists cannot perform dissection, they are subject to a greater scientific discipline than that required of anatomists.
If a decision scientist tried to account for an individual’s purchases
of bananas as the direct result of something like an “appetite for fruit,” we would not know how to test this hypothesis—that is, we would not know how to independently “look for” such an appetite, even if we had a scalpel and an open, anesthetized brain. Such unob20
Machina
servable constructs like appetites, utility, and preferences can—and do—play a role in scientific decision theory, but only as inside links in a causal chain that ultimately starts with fully observable independent variables and ultimately ends with fully observable dependent variables.
For example, given the joint hypothesis that well-defined commodity
preferences exist and are also stable from day to day, standard consumer theory allows us to infer enough information about these preferences from an individual’s past demand behavior to be able to make refutable predictions about their future demand behavior, even for some combinations of prices and income never before observed.
In the following sections, we shall see that in passing from choice over certain commodity bundles to choice over uncertain prospects (either “objective lotteries” or “subjective acts”), hypotheses involving the unobservable constructs of commodity preferences and utility functions
can be replaced by hypotheses involving the unobservable constructs
of risk preferences and beliefs, which also link observable independent to observable dependent variables. Whether the notion of “states of nature” can similarly serve remains to be discussed.
Scientific Modeling “From the Outside In”
Scientific Modeling “From the Outside In”
The human decision-making process may well be one of the most complicated systematic phenomena in the universe. In terms of the point of view of the scientific observer, it is certainly unique. On the one hand, a scientist trying to model this process is like an anatomist in the days before anesthesia and vivisection—scientists can observe and to some extent even control external influences on a system, and can observe the resulting behavior of the system as a whole, but they cannot
“get inside” to observe its constituent parts at work. On the other hand, every scientist is a human decision maker with powers of self-consciousness and self-reflection. However, self-reflection of our deci-sion-making processes has not produced that much more “hard science”
than has, say, self-reflection of our breathing or digestive processes.
While advances in neuroscience may ultimately do for decision theory what vivisection did for anatomy, decision theory currently remains very much a “black box” science. Although decision theorists can (and do) use introspection to suggest theories and hypotheses, the rigorous science consists of specifying mutually observable independent
variables (in particular, the objects of choice available for selection),
mutually observable dependent variables (the selected alternative), and refutable hypotheses linking the two. In other words, choice theory attempts to explain why particular alternatives are selected from a set of available choices.
The human decision-making process may well be one of the most complicated systematic phenomena in the universe. In terms of the point of view of the scientific observer, it is certainly unique. On the one hand, a scientist trying to model this process is like an anatomist in the days before anesthesia and vivisection—scientists can observe and to some extent even control external influences on a system, and can observe the resulting behavior of the system as a whole, but they cannot
“get inside” to observe its constituent parts at work. On the other hand, every scientist is a human decision maker with powers of self-consciousness and self-reflection. However, self-reflection of our deci-sion-making processes has not produced that much more “hard science”
than has, say, self-reflection of our breathing or digestive processes.
While advances in neuroscience may ultimately do for decision theory what vivisection did for anatomy, decision theory currently remains very much a “black box” science. Although decision theorists can (and do) use introspection to suggest theories and hypotheses, the rigorous science consists of specifying mutually observable independent
variables (in particular, the objects of choice available for selection),
mutually observable dependent variables (the selected alternative), and refutable hypotheses linking the two. In other words, choice theory attempts to explain why particular alternatives are selected from a set of available choices.
TWO EXTRAORDINARY NONSCIENTISTS
TWO EXTRAORDINARY NONSCIENTISTS
Almost 20 years ago, I briefly knew a man by the name of Craig. Although he died about a year after I met him, I’ve thought about him ever since. Craig had this uncanny ability to converse with a person for a few minutes, and then announce what make and model of car they drove. Neither I, nor anyone I ever spoke to, had ever seen him get it wrong. Craig was never able to explain how he did it, and his unique ability followed him to the grave.
What Craig had perfected was an impressive skill—perhaps even an art—but it was not science. It was not science because it was not a procedure that he could verbally communicate or write down, so that other people in other places or other times could do it also. One of the defining features of scientific activity is that it generates a body of knowledge and techniques that can be communicated and utilized by others in this way.
I also knew a woman named Tula with an equally impressive ability.
Tula was able to predict how well a person’s day would go, based on the shape, size, and color of the aura they emitted in the morning. And in contrast to Craig, she could even explain the specifics of her method. For example, if your aura was round and blue, you would have good luck all day. But if it was square and yellow, then you’d best go back home and stay in bed. Tula had prepared a chart with the complete
relationship between properties of your aura and the upcoming features of your day, so if you had a copy of the chart, you just needed a daily reading of your aura. Although Tula’s success rate wasn’t per17
18 Machina
fect (like Craig’s was), it still compared favorably with standard medical,
meteorological, and macroeconomic predictions, and most of her friends would stop by each morning for a quick reading of their aura, and then go away to consult their chart.
By constructing and distributing her chart, Tula had codified and communicated features of her technique in a way that Craig never could. But since Tula was the only one who could see these auras, what she was doing still was not science. An activity is not science unless it involves techniques that others can also apply as well as variables that others can observe.
The purpose of this chapter is to examine one of the most important
theoretical constructs of modern decision theory—namely, the concept of states of the world or states of nature—from the point of view of these and similar scientific considerations. Are states of nature inherently descriptive or prescriptive objects? Do individuals making choices under uncertainty face these states of nature, or do they create them? Are states external and independently observable, like an indi-vidual’s commodity demand levels, or are they internal and not directly observable, like utility or marginal utility levels? In addressing these questions, I will offer an overview of how researchers have sought to represent the concept of uncertainty, from the original formulation of probabilities and “objective uncertainty” in the seventeenth century, through Leonard Savage’s twentieth century formulation of states of nature and “subjective uncertainty,” to current work which seeks to eliminate—or at least redefine—the distinction between objective and subjective uncertainty. The following section presents some scientific issues common to all theories of choice, whether under certainty or uncertainty. The next two sections sketch out the current theories of choice under objective and subjective uncertainty. After that, I address the question of whether states of nature should be considered descriptive
or prescriptive constructs, and then I consider scientific issues related to the observability and measurement of states of nature. The final section concludes with current work on the relationship between subjective and objective uncertainty.
Almost 20 years ago, I briefly knew a man by the name of Craig. Although he died about a year after I met him, I’ve thought about him ever since. Craig had this uncanny ability to converse with a person for a few minutes, and then announce what make and model of car they drove. Neither I, nor anyone I ever spoke to, had ever seen him get it wrong. Craig was never able to explain how he did it, and his unique ability followed him to the grave.
What Craig had perfected was an impressive skill—perhaps even an art—but it was not science. It was not science because it was not a procedure that he could verbally communicate or write down, so that other people in other places or other times could do it also. One of the defining features of scientific activity is that it generates a body of knowledge and techniques that can be communicated and utilized by others in this way.
I also knew a woman named Tula with an equally impressive ability.
Tula was able to predict how well a person’s day would go, based on the shape, size, and color of the aura they emitted in the morning. And in contrast to Craig, she could even explain the specifics of her method. For example, if your aura was round and blue, you would have good luck all day. But if it was square and yellow, then you’d best go back home and stay in bed. Tula had prepared a chart with the complete
relationship between properties of your aura and the upcoming features of your day, so if you had a copy of the chart, you just needed a daily reading of your aura. Although Tula’s success rate wasn’t per17
18 Machina
fect (like Craig’s was), it still compared favorably with standard medical,
meteorological, and macroeconomic predictions, and most of her friends would stop by each morning for a quick reading of their aura, and then go away to consult their chart.
By constructing and distributing her chart, Tula had codified and communicated features of her technique in a way that Craig never could. But since Tula was the only one who could see these auras, what she was doing still was not science. An activity is not science unless it involves techniques that others can also apply as well as variables that others can observe.
The purpose of this chapter is to examine one of the most important
theoretical constructs of modern decision theory—namely, the concept of states of the world or states of nature—from the point of view of these and similar scientific considerations. Are states of nature inherently descriptive or prescriptive objects? Do individuals making choices under uncertainty face these states of nature, or do they create them? Are states external and independently observable, like an indi-vidual’s commodity demand levels, or are they internal and not directly observable, like utility or marginal utility levels? In addressing these questions, I will offer an overview of how researchers have sought to represent the concept of uncertainty, from the original formulation of probabilities and “objective uncertainty” in the seventeenth century, through Leonard Savage’s twentieth century formulation of states of nature and “subjective uncertainty,” to current work which seeks to eliminate—or at least redefine—the distinction between objective and subjective uncertainty. The following section presents some scientific issues common to all theories of choice, whether under certainty or uncertainty. The next two sections sketch out the current theories of choice under objective and subjective uncertainty. After that, I address the question of whether states of nature should be considered descriptive
or prescriptive constructs, and then I consider scientific issues related to the observability and measurement of states of nature. The final section concludes with current work on the relationship between subjective and objective uncertainty.
CATASTROPHE PLANNING
CATASTROPHE PLANNING
Accidents do happen despite the best intentions and most effective efforts to forestall such eventualities. And the response to the bad news is probably the most critical component of any loss reduction strategy.
In the immediate aftermath of the Rouge River powerhouse catastrophe,
William Clay Ford Jr. dispatched his personal aide, with credit card in hand, to track down the victims’ families and do whatever was required to help out. The company worked with its suppliers to procure
Risk and Risk Management 15
electrical switching equipment and to obtain portable boilers for steam. Detroit Edison built an outdoor substation—in a week—to supply the power necessary to get the Rouge River complex back on line. The result was a triumph in loss reduction—a potentially catastrophic business
interruption scenario truncated to a one-week hiccup on the production
line.
There are many other examples of the importance of catastrophe planning, good and bad. For example, back in 1986, when a still unidentified
individual replaced the painkiller in several bottles of Tylenol capsules with cyanide, the result was the death of an innocent consumer.
Johnson & Johnson, the maker of Tylenol, didn’t attempt to deflect blame (after all, they hadn’t adulterated the capsules) or otherwise
temporize. They immediately recalled all the capsules from store shelves—even those that were clearly untainted—and then designed the generation of tamper-proof containers still in use today. This is a textbook loss-reduction strategy—timely, aggressive, and (while costly in the short run) effective.
In contrast, consider the strategy of Johns-Manville, once the world’s biggest producer of asbestos, which, as we noted earlier, collapsed
under the weight of litigation from asbestos claims in 1982. Johns-Manville’s apparent decision to ignore the risks of asbestos exposure to its workers, long after the evidence indicated that management
may have suspected a link between asbestos exposures in the workplace and worker health, resulted in lives ruined and lost. The cost to Manville and its shareholders was ultimately that of corporate bankruptcy.
Dan Sobczynski offers some sound advice: “Either manage the risk, or it will manage you,” he says, “and, when it does, the loss will happen when you are least prepared” (Financial Times 1999).
Accidents do happen despite the best intentions and most effective efforts to forestall such eventualities. And the response to the bad news is probably the most critical component of any loss reduction strategy.
In the immediate aftermath of the Rouge River powerhouse catastrophe,
William Clay Ford Jr. dispatched his personal aide, with credit card in hand, to track down the victims’ families and do whatever was required to help out. The company worked with its suppliers to procure
Risk and Risk Management 15
electrical switching equipment and to obtain portable boilers for steam. Detroit Edison built an outdoor substation—in a week—to supply the power necessary to get the Rouge River complex back on line. The result was a triumph in loss reduction—a potentially catastrophic business
interruption scenario truncated to a one-week hiccup on the production
line.
There are many other examples of the importance of catastrophe planning, good and bad. For example, back in 1986, when a still unidentified
individual replaced the painkiller in several bottles of Tylenol capsules with cyanide, the result was the death of an innocent consumer.
Johnson & Johnson, the maker of Tylenol, didn’t attempt to deflect blame (after all, they hadn’t adulterated the capsules) or otherwise
temporize. They immediately recalled all the capsules from store shelves—even those that were clearly untainted—and then designed the generation of tamper-proof containers still in use today. This is a textbook loss-reduction strategy—timely, aggressive, and (while costly in the short run) effective.
In contrast, consider the strategy of Johns-Manville, once the world’s biggest producer of asbestos, which, as we noted earlier, collapsed
under the weight of litigation from asbestos claims in 1982. Johns-Manville’s apparent decision to ignore the risks of asbestos exposure to its workers, long after the evidence indicated that management
may have suspected a link between asbestos exposures in the workplace and worker health, resulted in lives ruined and lost. The cost to Manville and its shareholders was ultimately that of corporate bankruptcy.
Dan Sobczynski offers some sound advice: “Either manage the risk, or it will manage you,” he says, “and, when it does, the loss will happen when you are least prepared” (Financial Times 1999).
MITIGATION AND CONTROL
MITIGATION AND CONTROL
After the risk exposure has been assessed, the next step is to consider
how one deals with it. Continuing with our street-crossing example,
one possibility would be to avoid the risk entirely and not cross the street at all (a wise strategy if the road in question were, say, Interstate 94 at rush hour). Alternatively, if we decide to proceed, the question might be the following: do we jaywalk and cross the street now, or
Risk and Risk Management 13
stroll down to the traffic signal and wait for the green light? Each of these alternatives represents an economic decision, weighing the cost of the strategy against the potential benefits.
Generically, mitigating a risk exposure entails the identification of tactics either to reduce the probability of a bad outcome, or to reduce the magnitude of a loss, should a bad outcome occur. The former types of activities, referred to as loss prevention measures, would include the cross-at-the-intersection option discussed above, or, in a more mundane
industrial setting, the inspection of electrical wiring to reduce the probability of an electrical fire. Indeed, most of the risk mitigation strategies that come easily to mind are designed to keep us out of trouble
in the first place—don’t put the gasoline can next to the furnace, don’t smoke in bed, lock your doors before you retire for the night. Loss reduction, on the other hand, describes the class of risk mitigation activities designed to reduce the magnitude of a loss, should one occur. The standard example here would be the installation of sprinklers in a warehouse, which doesn’t reduce the probability of a fire starting but, rather, mitigates the damages that result from the fire.
The explosion of boiler number six at the River Rouge powerhouse occurred during a maintenance shutdown. As far as can be determined, a valve unintentionally left open allowed natural gas to flow into the boiler, which was quickly ignited by the electrostatic scrubbers located in the boiler’s chimney.
In retrospect, it appears that the tragedy stemmed from a lack of attention paid to issues of risk mitigation during routine episodes of maintenance. Not only was the act of shutting down the boilers rare, but apparently there were no written procedures or checklists to guide the process. Employees who had not been trained in shutting off the boilers and who had last received an equipment manual in 1997, had to shut off over 30 (unlabeled) natural gas valves throughout the powerhouse
complex. They missed one, and the rest is history.
We make trade-offs in our personal and business lives between the burden of risk exposure and the cost of risk mitigation. Financing the costs associated with a bad outcome becomes the question. In personal settings, the risk financing strategy generally adopted is that of risk shifting to a third party, usually an insurance company (think about the collision and liability insurance on your car, homeowner’s insurance, or the warranty on a new appliance). The problem with this type of risk
14 Crocker
transfer, though, is that it creates what is known in economics as a “moral hazard.”
A colleague of mine kept a sailboat moored off the end of his dock on Long Island Sound. One day, during casual conversation, I asked about his strategy for dealing with storms and the like—as a boat owner myself, I was aware (risk identification and quantification) of the effects of heavy wave action on a boat banging against a dock. He responded that he wasn’t worried because he had insurance and he never took the boat out of the water until the end of the season. The problem here, of course, is that if one is fully insured against a loss, then one has no incentive to take (privately costly) actions to reduce one’s risk exposure. Insurance companies, not surprisingly, have figured
this out.
When my teen-aged son finally made enough money to purchase a car, it turned out that the machine of his dreams was a 1994 Camaro Z28, with a 5.7 liter V-8 engine and 270 horsepower. You might think that no insurer in their right mind would write coverage in a situation like this, but you would be wrong. An automobile insurer in Michigan was willing to provide liability coverage at a finite premium. But, there was a catch—no coverage for collision damage.3 Effectively, he has a 100 percent deductible if he wraps the car around a tree.
This retained risk has “incentivized” my son to drive carefully. This is generally the trade-off that you will find in your personal and professional risk financing decisions—increased investment in risk elimination reduces the premiums you pay per dollar of coverage, but the down side is that you are exposed to more risk.
After the risk exposure has been assessed, the next step is to consider
how one deals with it. Continuing with our street-crossing example,
one possibility would be to avoid the risk entirely and not cross the street at all (a wise strategy if the road in question were, say, Interstate 94 at rush hour). Alternatively, if we decide to proceed, the question might be the following: do we jaywalk and cross the street now, or
Risk and Risk Management 13
stroll down to the traffic signal and wait for the green light? Each of these alternatives represents an economic decision, weighing the cost of the strategy against the potential benefits.
Generically, mitigating a risk exposure entails the identification of tactics either to reduce the probability of a bad outcome, or to reduce the magnitude of a loss, should a bad outcome occur. The former types of activities, referred to as loss prevention measures, would include the cross-at-the-intersection option discussed above, or, in a more mundane
industrial setting, the inspection of electrical wiring to reduce the probability of an electrical fire. Indeed, most of the risk mitigation strategies that come easily to mind are designed to keep us out of trouble
in the first place—don’t put the gasoline can next to the furnace, don’t smoke in bed, lock your doors before you retire for the night. Loss reduction, on the other hand, describes the class of risk mitigation activities designed to reduce the magnitude of a loss, should one occur. The standard example here would be the installation of sprinklers in a warehouse, which doesn’t reduce the probability of a fire starting but, rather, mitigates the damages that result from the fire.
The explosion of boiler number six at the River Rouge powerhouse occurred during a maintenance shutdown. As far as can be determined, a valve unintentionally left open allowed natural gas to flow into the boiler, which was quickly ignited by the electrostatic scrubbers located in the boiler’s chimney.
In retrospect, it appears that the tragedy stemmed from a lack of attention paid to issues of risk mitigation during routine episodes of maintenance. Not only was the act of shutting down the boilers rare, but apparently there were no written procedures or checklists to guide the process. Employees who had not been trained in shutting off the boilers and who had last received an equipment manual in 1997, had to shut off over 30 (unlabeled) natural gas valves throughout the powerhouse
complex. They missed one, and the rest is history.
We make trade-offs in our personal and business lives between the burden of risk exposure and the cost of risk mitigation. Financing the costs associated with a bad outcome becomes the question. In personal settings, the risk financing strategy generally adopted is that of risk shifting to a third party, usually an insurance company (think about the collision and liability insurance on your car, homeowner’s insurance, or the warranty on a new appliance). The problem with this type of risk
14 Crocker
transfer, though, is that it creates what is known in economics as a “moral hazard.”
A colleague of mine kept a sailboat moored off the end of his dock on Long Island Sound. One day, during casual conversation, I asked about his strategy for dealing with storms and the like—as a boat owner myself, I was aware (risk identification and quantification) of the effects of heavy wave action on a boat banging against a dock. He responded that he wasn’t worried because he had insurance and he never took the boat out of the water until the end of the season. The problem here, of course, is that if one is fully insured against a loss, then one has no incentive to take (privately costly) actions to reduce one’s risk exposure. Insurance companies, not surprisingly, have figured
this out.
When my teen-aged son finally made enough money to purchase a car, it turned out that the machine of his dreams was a 1994 Camaro Z28, with a 5.7 liter V-8 engine and 270 horsepower. You might think that no insurer in their right mind would write coverage in a situation like this, but you would be wrong. An automobile insurer in Michigan was willing to provide liability coverage at a finite premium. But, there was a catch—no coverage for collision damage.3 Effectively, he has a 100 percent deductible if he wraps the car around a tree.
This retained risk has “incentivized” my son to drive carefully. This is generally the trade-off that you will find in your personal and professional risk financing decisions—increased investment in risk elimination reduces the premiums you pay per dollar of coverage, but the down side is that you are exposed to more risk.
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