Agent Technology for e-Commerce

Agent Technology for e-Commerce Appendix A: Introduction to Decision Theory Maria Fasli http://cswww.essex.ac.uk/staff/mfasli/ATe-Commerce.htm

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

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

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 Axioms • 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

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 L=[p1,o1;p2,o2;…;pn,on]

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): P(ab)=P(a|b)P(b) • Independence P(a|b)=P(a) and P(b|a)=P(b) or P(ab)=P(a)P(b)

Bayes’ rule The product rule can be written as: P(ab)=P(a|b)P(b) P(ab)=P(b|a)P(a) By equating the right-hand sides: This is known as Bayes’ rule

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

An alternative representation of payoffs – tree diagram

Admissibility • 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

Non-probabilistic decision-making under uncertainty • The maximin rule • The maximax rule • The minimax loss Payoff table Loss table

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

Utilities • 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

Preferences • 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’] )

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

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

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

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

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

Utility function of risk-averse agent

Utility function of a risk-prone agent

Utility function of a risk-neutral agent

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: u(x,y)=wxu(x)+wyu(y)+(1-wx-wy)u(x)u(y)

Agent Technology for e-Commerce