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15502 results in Philosophy of science

Page 158 of 776

  • 5 - Utility

  • Martin Peterson, Texas A & M University
  • Book:
    An Introduction to Decision Theory
    Published online:
    28 May 2018
    Print publication:
    30 March 2017, pp 97-123

  • Summary

    It is hard to think of any minimally reasonable decision rule that totally ignores how much utility an act brings about. However, the concept of utility has many different technical meanings, and it is important to keep these different meanings apart. In Chapter 2 we distinguished three fundamentally different kinds of measurement scales. All scales are numerical, i.e. utility is represented by real numbers, but the information conveyed by the numbers depends on which type of scale is being used.

  • 1. Ordinal scales (“10 is better than 5”)

  • 2. Interval scales (“the difference between 10 and 5 equals that between 5 and 0”)

  • 3. Ratio scales (“10 is twice as valuable as 5”)

    • In ordinal scales, better objects are assigned higher numbers. However, the numbers do not reflect any information about differences or ratios between objects. If we wish to be entitled to say, for instance, that the difference between ten and five units is exactly as great as the difference between five and zero units, then utility has to be measured on an interval scale. Furthermore, to be entitled to say that ten units of utility is twice as much as five, utility must be measured on a ratio scale.

    Arguably, utility cannot be directly revealed by introspection. We could of course ask people to estimate their utilities, but answers gathered by this method would most certainly be arbitrary. Some more sophisticated method is needed. So how on earth is it possible to assign precise numbers to outcomes and acts that accurately reflect their value? And how can this process be a scientific one, rather than a process that is merely arbitrary and at best based on educated guesses?

    How to Construct an Ordinal Scale

    Let us first show how to construct an ordinal utility scale. To make the discussion concrete, imagine that you have a collection of crime novels, and that you wish to assign ordinal utilities to each book in your collection. To avoid a number of purely mathematical obstacles we assume that the number of books in the collection is finite.

  • 1 - Introduction

  • Martin Peterson, Texas A & M University
  • Book:
    An Introduction to Decision Theory
    Published online:
    28 May 2018
    Print publication:
    30 March 2017, pp 1-16

  • Summary

    On September 6, 1492, Christopher Columbus set off from the Canary Islands and sailed westward in an attempt to find a new trade route between Europe and the Far East. On October 12, after five weeks of sailing across the Atlantic, land was sighted. Columbus had never been to the Far East, so when he landed in Central America (“the West Indies”) he believed that he had indeed discovered a new route to the Far East. Not until twenty-nine years later did Magellan finally discover the westward route to the Far East by sailing south around South America.

    Columbus’ decision to sail west from the Canary Islands was arguably one of the bravest decisions ever made by an explorer. But was it rational? Unlike some of his contemporaries, Columbus believed that the earth is a rather small sphere. Based on his geographical assumptions, he estimated the distance from Europe to East India to total 2,300 miles. The actual distance is about 12,200 miles, which is more than five times farther than Columbus thought. In the fifteenth century, no ship would have been able to carry provisions for such a long journey. Had America not existed, or had the earth been flat, Columbus would certainly have faced a painful death. Was it really worth risking everything for the sake of finding a new trade route?

    This book is about decision theory. Decision theory is the theory of rational decision making. Columbus’ decision to set off westwards across an unknown ocean serves as a fascinating illustration of what decision theory is all about. A decision maker, in this case Columbus, chooses an act from a set of alternatives, such as sailing westwards or staying at home. The outcome depends on the true state of the world, which in many cases is only partially known to the decision maker. For example, had the earth been a modest-sized sphere mostly covered by land and a relatively small and navigable sea, Columbus’ decision to sail westwards would have made him rich and famous, because the King and Queen of Spain had promised him 10% of all revenue gained from a new trade route. However, Columbus’ geographical hypothesis turned out to be false.

  • 9 - Technology and Autism

  • Clive Lawson, University of Cambridge
  • Book:
    Technology and Isolation
    Published online:
    14 April 2017
    Print publication:
    30 March 2017, pp 129-159

  • Summary

    It is uncontentious to suggest that those with autism tend to have some kind of special relationship with technology. An abundance of reports from parents and clinicians take it largely for granted that children with autism are attracted or drawn to various kinds of technological devices (Colby, 1973). More formal evidence for such a relationship has come from research into the use of assistive technologies (Wainer and Ingersoll, 2011, Goldsmith and LeBlanc, 2004). Elsewhere, support comes from research into the profiles of autistic children, where a disproportionate number of those with autism are male and offspring of engineers and mathematicians (Wheelwright and Baron-Cohen, 2001). Support is also found in claims that there are dramatic increases in the prevalence of autism in high-technology regions such as Silicon Valley (Silberman, 2000).

    A central feature of these reports is the idea that technology presents a more comfortable or more manageable interface between those with autism and aspects of a social world that are often experienced as mysterious and unsettling. Whilst a few studies exist that focus upon implications that follow from such a relationship for our understanding of autism, as far as I know there are none that ask if such a relationship might tell us something about the nature of technology. This is the general motivation of this chapter.

    More specifically, this chapter summarises the main features of autism as it is currently understood along with a discussion of prominent theories used to explain it. An account of these broad features is then given in terms of the conception of technology developed in preceding chapters, focusing upon the nature of the relationship between those with autism and technology. A central focus is upon the different kinds of isolatability that exist in reality and that are particularly relevant to an understanding of technology. My main argument is then that difficulties in coping with inherently unisolatable phenomena both prompt an attraction to technological devices and encourage particular kinds of relations to it. Finally, some implications are considered.

  • 10 - Risk Aversion

  • Martin Peterson, Texas A & M University
  • Book:
    An Introduction to Decision Theory
    Published online:
    28 May 2018
    Print publication:
    30 March 2017, pp 220-233

  • Summary

    Imagine you have just won your first professional golf tournament. The chairman of the United States Golf Association offers you to either collect the $1,000,000 prize check right away or toss a fair coin. If the coin lands heads he will double the prize check, but if it lands tails you lose the money and have to walk away with $0. As a relatively poor newcomer to the world of professional golf you strictly prefer $1,000,000 to the actuarially fair gamble in which you either double or lose your prize money. This is hardly controversial. It may take years before you win your next tournament. Few would question that decision makers are sometimes rationally permitted to be risk averse, especially if the stakes are high. That said, it remains to explain how risk aversion should be defined and analyzed, and to determine whether it is always rationally permissible to be risk averse.

    The term “risk aversion” has several different but interconnected meanings in decision theory. Some of these are compatible with the principle of maximizing expected utility, while others are not. In what follows we shall discuss three of the most influential notions of risk aversion discussed in the literature:

  • 1. Aversion against actuarial risks

  • 2. Aversion against utility risks

  • 3. Aversion against epistemic risks

    • Each of these notions of risk aversion can be characterized formally. As we will see shortly, this makes it easier to assess their applicability and limitations.

    Actuarial Risk Aversion

    Kenneth Arrow, winner of the Nobel Prize in Economics, characterizes what we may call actuarial risk aversion as follows: “[a] risk averter is defined as one who, starting from a position of certainty, is unwilling to take a bet which is actuarially fair.” A golfer who is unwilling to accept a bet worth $1,000,000 on the toss of a fair coin is risk averse in this sense. For another example, imagine that you are offered a choice between getting three apples for sure and a lottery that gives you a fifty-fifty chance of winning either six apples or nothing. Unless you desperately need six apples for making an apple pie it seems rationally permissible to prefer three apples for sure over the actuarially fair lottery.

  • 1 - Technology Questions

  • Clive Lawson, University of Cambridge
  • Book:
    Technology and Isolation
    Published online:
    14 April 2017
    Print publication:
    30 March 2017, pp 1-16

  • Summary

    It is often suggested that we live in a technological age. Although it is rarely made clear exactly what this statement means, or why or in what ways previous ages are thought not to be technological, most of us seem to agree that technology plays an important role in our lives. We may also agree that this role is becoming increasingly important. In all kinds of daily activities such as buying a bus ticket, guessing how the weather will change, listening to music, paying for shopping at a supermarket, archiving family photos or borrowing a book from the library, we all experience a constant prodding to our routine or ‘normal’ ways of doing things that can be traced to some or other new technological development. For the most part, moreover, such developments come into being in ways, and for reasons, that lie outside of our control. In other words, we all, through our everyday activities, experience technology's power as an external agent of change.

    This experience suggests a range of important questions. To what extent is it possible or desirable to influence the introduction of new technology? To what extent do different technologies determine or constrain the kinds of social changes that follow or accommodate them? Do societies have broad trends or characteristics that are related to the amount or form of technology that have emerged within them – for example, can it be said that people are more or less connected to each other in virtue of the technology they use? Does technology bring with it opportunities for a better life or tend to smuggle in unnecessary problems? Does the form or speed of change of different technologies matter? Is technology always neutral, only taking on good or bad features in some particular context of use? Is it even possible or meaningful to talk in general about ‘technology’ at all?

    Such questions will be familiar to many if not most of us. They have been the lifeblood of science fiction since the beginning of the genre. More formally, or academically, such questions have occupied a wide variety of social theorists since at least the time of the ancient Greeks. Recently, contributions concerned with such questions have been organised together as constituents of a ‘philosophy of technology’.

  • 11 - Game Theory I: Basic Concepts and Zero-sum Games

  • Martin Peterson, Texas A & M University
  • Book:
    An Introduction to Decision Theory
    Published online:
    28 May 2018
    Print publication:
    30 March 2017, pp 234-262

  • Summary

    Game theory studies decisions in which the outcome depends partly on what other people do. Chess is a paradigmatic example. Before I make a move, I always carefully consider what my opponent's best response will be, and if the opponent can respond by doing something that will force a checkmate, she can be fairly certain that I will do my best to avoid that move. Both I and my opponent know all this, and this assumption of common knowledge of rationality (CKR) determines which move I will eventually choose, as well as how my opponent will respond. I do not consider the move to be made by my opponent to be a state of nature that occurs with a fixed probability independently of what I do. On the contrary, the move I make effectively decides my opponent's next move.

    Chess is, however, not the best game to study for newcomers to game theory. This is because it is such a complex game with many possible moves. Like other parlor games, such as bridge, monopoly and poker, chess is also of limited practical significance. In this chapter we will focus on other games, which are easier to analyze but nevertheless of significant practical importance. Consider, for example, two hypothetical supermarket chains, Row and Col. Both have to decide whether to set prices high and thereby try to make a good profit from every item sold, or go for low prices and make their profits from selling much larger quantities. Naturally, each company's profit depends on whether the other company decides to set its prices high or low.

    If both companies sell their goods at high prices, they will both make a healthy profit of $100,000. However, if one company goes for low prices and the other for high prices, the company retailing for high prices will sell just enough to cover their expenses and thus make no profit at all ($0), whereas the other company will sell much larger quantities and make $120,000. Furthermore, if both companies set their prices low each of them will sell equally much but make a profit of only $10,000. Given that both companies wish to maximize their profit, what would you advise them to do? Consider the game matrix in Table 11.1.

  • Appendix: Glossary

  • Martin Peterson, Texas A & M University
  • Book:
    An Introduction to Decision Theory
    Published online:
    28 May 2018
    Print publication:
    30 March 2017, pp 323-328

  • Summary

    Axiom: An axiom is a fundamental premise of an argument for which no further justification is given. Example: According to the asymmetry axiom, no rational agent strictly prefers x to y and y to x.

    Bargaining problem: The bargaining problem is a cooperative game with infinitely many Nash equilibria, which serves as a model for a type of situation that arises in many areas of society: A pair of players are offered to split some amount of money between the two of them. Each player has to write down his or her demand and place it in a sealed envelope. If the amounts they demand sum to more than the total amount available the players will get nothing; otherwise each player will get the amount he or she demanded. The players are allowed to communicate and form whatever binding agreements they wish. A general solution to this problem was offered by Nash, who based his proposal on a small set of intuitively plausible axioms.

    Bayes’ theorem: Bayes’ theorem is an undisputed mathematical result about the correct way to calculate conditional probabilities. It holds that the probability of B given A equals the probability of B times the probability of A given B, divided by the following two terms: the probability of B times the probability of A given B and the probability of not-B times the probability of A given not-B. Put in symbols:

    Bayesianism: The term ‘Bayesianism’ has many different meanings in decision theory, statistics and the philosophy of those disciplines. Most Bayesian accounts of decision theory and statistics can be conceived of as claims about the correct way to apply Bayes’ theorem in various real-life contexts. In decision theory, Bayesianism is particularly closely associated with the view that probabilities are subjective and that rational decision makers seek to maximize subjective expected utility.

    Cardinal scale: Cardinal scales are used when we measure objects numerically and differences or ratios between measurement points are preserved across all possible transformations of the scale. Example: Time, mass, money and temperature can be measured on cardinal scales. Cardinal scales can be divided into two categories, namely interval scales and ratio scales.

  • 7 - Technology and the Extension of Human Capabilities

  • Clive Lawson, University of Cambridge
  • Book:
    Technology and Isolation
    Published online:
    14 April 2017
    Print publication:
    30 March 2017, pp 99-113

  • Summary

    The main argument of this chapter is that another distinguishing feature of technology lies in the role that technological artefacts play in extending human capabilities. However, the sense in which I understand technology to extend human capabilities requires a fair amount of clarification. In order to provide this, I need to distinguish the argument I am making from similar ideas that exist in the philosophy of technology literature. The literature I have in mind here is that in which technology is conceived of as the more or less direct extension of human faculties. Specifically, I first discuss some prominent extension theories, including the classic accounts of Ernst Kapp and Marshall McLuhan as well as some more recent contributions. The intention is not only to clarify the sense of extension I have in mind but also to introduce some ideas that I return to later on. In the second section, I try to integrate these extension ideas into the general conception of technology developed in the previous two chapters before finally drawing out some implications of the account I defend. To repeat, the main task is to indicate how the idea of extension acts to mark off, at least partially, technological from other artefacts, along with suggesting some of the benefits that follow from such a demarcation.

    Extension Theories of Technology

    By extension theory, I mean any theory in which technological artefacts are conceived of as some kind of extension of the human organism by way of replicating, amplifying, or supplementing bodily or mental faculties or capabilities. This basic idea of extension recurs throughout the study of technology and is found in discussions of technology that go back at least as far back as Aristotle. The more systematic treatments tend to emphasise one or more of three features: a focus upon the direct, often very mechanical, extension of ‘physical’ faculties; a focus upon the extension of cognitive (especially information processing) capabilities; the extension of human agents’ ‘will’ or intentions. I shall illustrate each feature by briefly referring in turn to the work of Ernst Kapp, Marshall McLuhan and David Rothberg.

    The first occurrence of a detailed and sustained example of an extension theory is that provided by Ernst Kapp (1877).

  • 8 - Bayesianism and Pragmatic Arguments

  • Martin Peterson, Texas A & M University
  • Book:
    An Introduction to Decision Theory
    Published online:
    28 May 2018
    Print publication:
    30 March 2017, pp 174-205

  • Summary

    There are almost as many definitions of Bayesianism as there are decision theorists. The following broad definition proposed by Richard Bradley is, however, a fairly uncontroversial characterization of the main idea.

    Bayesian decision theories are formal theories of rational agency: they aim to tell us both what the properties of a rational state of mind are … and what action it is rational for an agent to perform, given her state of mind.

    (Bradley 2007: 233)

    According to this definition, Bayesianism has two distinct components. The first tells you what your state of mind ought to be like, while the second tells you how you ought to act given that state of mind. Let us call the two components the epistemic and the deliberative component, respectively. The epistemic component of Bayesianism is a claim about what rational agents ought to believe, and which combinations of beliefs and desires are rationally permissible. In essence, the theory holds that one is free to believe whatever one wishes as long as one's beliefs can be represented by a subjective probability function, and those beliefs are updated in accordance with Bayes’ theorem (see Chapter 6). This means that Bayesian epistemology offers virtually no substantial advice on how one ought to go about when exploring the world. The theory merely provides a set of structural restrictions on what it is permissible to believe, and how one is permitted to revise those beliefs in light of new information. Bayesians thus maintain that all beliefs come in degrees, and that the theory of subjective probability provides an accurate account of how degrees of belief ought to be revised. (Some Bayesians advocate objective versions of the theory, according to which probabilities are objective features of the world such as relative frequencies or physical propensities. Naturally, the mathematical framework for how probabilities ought to be updated is the same.)

  • 5 - The Sociality of Artefacts

  • Clive Lawson, University of Cambridge
  • Book:
    Technology and Isolation
    Published online:
    14 April 2017
    Print publication:
    30 March 2017, pp 62-78

  • Summary

    The sense and degree to which technology can be understood as social is a point of significant tension in the existing literature. It was argued in Chapter 2 that the term ‘technology’ came into being to describe the largely material results of engineers and others in transforming the physical world. Thus the term gained currency as part of a shift away from the (social) activities of inventors and innovators, to the tangible (material, physical) results of such activities. On the other hand, it is often argued that one of the main strengths of recent (especially constructivist) theories of technology is their demonstration of the irreducibly social nature of technology (Brey, 1997, Bijker et al., 1987, MacKenzie and Wajcman, 1985b). In this context it is easy to see how the sense and degree to which technology is understood to be social has come to be something of a fault line in discussions of technology. This chapter is primarily intended to address these tensions.

    Several points of clarification, however, are required from the start. First, the apparent tension here does not arise because of a distinction between technology and technological artefact; it is not that technology is an activity and so social, whereas technological artefacts are material and so not social. Rather it is the sociality of artefacts that is contested. Secondly, the statement that technological artefacts are irreducibly social may appear rather obvious. Artefacts are made by people and so, in a sense, must be social. The more contested question, however, is whether or not, or in what ways, artefacts can be thought of as social in a more ongoing way, once they have been made. In other words, is there something about the ongoing mode of existence of artefacts that also depends upon the actions and interactions of human beings? This question has generated heated debate in recent years (Margolis and Laurence, 2007, Franssen et al., 2014) and I shall draw upon some of this debate where appropriate. In particular, the main points of contention are set out by focusing upon issues of identification, function and practice, before suggesting some advantages of couching the discussion of artefacts, and their sociality, in terms of a conception of positioning as discussed in Chapter 3.

The Meaning of the Wave Function
  • In Search of the Ontology of Quantum Mechanics
  • Shan Gao
  • Published online:
    13 April 2017
    Print publication:
    16 March 2017
  • At the heart of quantum mechanics lies the wave function, a powerful but mysterious mathematical object which has been a hot topic of debate from its earliest stages. Covering much of the recent debate and providing a comprehensive and critical review of competing approaches, this ambitious text provides new, decisive proof of the reality of the wave function. Aiming to make sense of the wave function in quantum mechanics and to find the ontological content of the theory, this book explores new ontological interpretations of the wave function in terms of random discontinuous motion of particles. Finally, the book investigates whether the suggested quantum ontology is complete in solving the measurement problem and if it should be revised in the relativistic domain. A timely addition to the literature on the foundations of quantum mechanics, this book is of value to students and researchers with an interest in the philosophy of physics.

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