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Agent Technology for e-Commerce. Appendix A: Introduction to Decision Theory Maria Fasli Decision theory. Decision theory is the study of making decisions that have a significant impact Decision-making is distinguished into:

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Agent Technology for e-Commerce

Appendix A: Introduction to Decision Theory

Maria Fasli

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Decision theory

Decision theory is the study of making decisions that have a significant impact

Decision-making is distinguished into:

  • Decision-making under certainty

  • Decision-making under noncertainty

    • Decision-making under risk

    • Decision-making under uncertainty

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Probability theory

  • Most decisions have to be taken in the presence of uncertainty

  • Probability theory quantifies uncertainty regarding the occurrence of events or states of the world

  • Basic elements of probability theory:

    • Random variables describe aspects of the world whose state is initially unknown

    • Each random variable has a domain of values that it can take on (discrete, boolean, continuous)

    • An atomic event is a complete specification of the state of the world, i.e. an assignment of values to variables of which the world is composed

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Probability space

  • The sample space S={e1,e2,…,en} which is a set of atomic events

  • The probability measure P which assigns a real number between 0 and 1 to the members of the sample space


  • All probabilities are between 0 and 1

  • The sum of probabilities for the atomic events of a probability space must sum up to 1

  • The certain event S (the sample space itself) has probability 1, and the impossible event which never occurs, probability 0

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Prior probability

  • In the absence of any other information, a random variable is assigned a degree of belief called unconditional or prior probability

  • P(X) denotes the vector consisting of the probabilities of all possible values that a random variable can take

  • If more than one variable is considered, then we have joint probability distributions

  • Lottery: a probability distribution over a set of outcomes


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Conditional probability

  • When we have information concerning previously unknown random variables then we use posterior or conditional probabilities: P(a|b) the probability of a given that we know b

  • Alternatively this can be written (the product rule):


  • Independence

    P(a|b)=P(a) and P(b|a)=P(b) or P(ab)=P(a)P(b)

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Bayes’ rule

The product rule can be written as:



By equating the right-hand sides:

This is known as Bayes’ rule

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Making decisions

Simple example: to take or not my umbrella on my way out

The consequences of decisions can be expressed in terms of payoffs

Payoff table

Loss table

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An alternative representation of payoffs – tree diagram

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  • An action is said to dominate another, if for each possible state of the world the first action leads to at least as high a payoff (or at least as small a loss) as the second one, and there is at least one state of the world in which the first action leads to a higher payoff (or smaller loss) than the second one

  • If one action dominates another, then the latter should never be selected and it is called inadmissible

Payoff table

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Non-probabilistic decision-making under uncertainty

  • The maximin rule

  • The maximax rule

  • The minimax loss

Payoff table

Loss table

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Probabilistic decision-making under uncertainty

  • The Expected Payoff (ER) rule dictates that the action with the highest expected payoff should be chosen

  • The Expected Loss (EL) rule dictates that the action with the smallest expected loss should be chosen

    If P(rain)=0.7 and P(not rain)=0.3 then:

    ER(carry umbrella) = 0.7(-£1)+0.3(-£1)=-£1

    ER(not carry umbrella) = 0.7(-£50)+0.3(-£0)=-£35

    EL(carry umbrella) = 0.7(£0)+0.3(£1)=£0.3

    EL(not carry umbrella) = 0.7(£49)+0.3(£0)=£34.3

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  • Usually the consequences of decisions are expressed in monetary terms

  • Additional factors such are reputation, time, etc. are also usually translated into money

  • Issue with the use of money to describe the consequences of actions:

    • If a fair coin comes up heads you win £1, otherwise you loose £0.75, would you take this bet?

    • If a fair coin comes up heads you win £1000, otherwise you loose £750, would you take this bet?

  • The value of a currency, differs from person to person

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  • The concept of preference is used to indicate that we would like/desire/prefer one thing over another

  • oo’ indicates that o is (strictly) preferred to o’

  • o ~ o’ indicates that an agent is indifferent between o and o’

  • o o’ indicates that o is (weakly) preferred to o’

  • Given any o and o’, then oo’, or o’o, or o ~ o’

  • Given any o, o’ and o’’, then if oo’ and o’o’’, then o’o’’

  • If oo’o’’, then there is a p such that [p,o;1-p,o’’] ~ o’

  • If o ~ o’, then [p,o; 1-p,o’’] ~ [p,o’; 1-p, o’’]

  • If oo’, then (pq [p,o;1-p,o’] [q,o;1-q,o’] )

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Utility functions

  • A utility function provides a convenient way of conveying information about preferences

  • If oo’, then u(o)>u(o’) and if o ~ o’ then u(o)=u(o’)

  • If an agent is indifferent between:

    (a) outcome o for certain and

    (b) taking a bet or lottery in which it receives o’ with probability p and o’’ with probability 1-p

    then u(o)=(p)u(o’)+(1-p)u(o’’)

  • Ordinal utilities

  • Cardinal utilities

  • Monotonic transformation

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Assessing a utility function

How can an agent assess a utility function?

  • Suppose most and least preferable payoffs are R+ and R- and

    u(R+)=1 and u(R-)=0

  • For any other payoff R, it should be:

    u(R+)  u(R)  u(R-) or 1 u(R)  0

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  • To determine the value of u(R) consider:

    • L1: Receive R for certain

    • L2: Receive R+ with probability p and R- with probability 1-p

  • Expected utilities:

    • EU(L1)=u(R)

    • EU(L2)=(p)u(R+ )+(1-p)u(R- )=(p)(1)+(1-p)(0)=p

  • If u(R)>p, L1 should be selected, whereas if u(R)<p, L2 should be selected, and if u(R)=p then the agent is indifferent between the two lotteries

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Utility and money

  • The value, i.e. utility, of money may differ from person to person

  • Consider the lottery

    • L1: receive £0 for certain

    • L2: receive £100 with probability p and -£100 with (1-p)

  • Suppose an agent decides that for p=0.75 is indifferent between the two lotteries, i.e. p>0.75 prefers lottery L2

  • The agent also assess u(-£50)=0.4 and u(£50)=0.9

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  • If p is fixed, the amount of money that an agent would need to receive for certain in L1 to make it indifferent between two lotteries can be determined. Consider:

    • L1: receive £x for certain

    • L2: receive £100 with probability 0.5 and -£100 with 0.5

  • Suppose x=-£30, then u(-£30)=0.5 and -£30 is considered to be the cash equivalent of the gamble involved in L2

  • The amount of £30 is called the risk premium – the basis of insurance industry

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Utility function of risk-averse agent

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Utility function of a risk-prone agent

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Utility function of a risk-neutral agent

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Multi-attribute utility functions

  • The utility of an action may depend on a number of factors

  • Multi-dimensional or multi-attribute utility theory deals with expressing such utilities

  • Example: you are made a set of job offers, how do you decide?

    u(job-offer) = u(salary) + u(location) +

    u(pension package) + u(career opportunities)

    u(job-offer) = 0.4u(salary) + 0.1u(location) +

    0.3u(pension package) + 0.2u(career opportunities)

    But if there are interdependencies between attributes, then additive utility functions do not suffice. Multi-linear expressions:


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