• Back to Profile
    •  

     


    … pour juger de ce que l'on doit faire pour obtenir un bien ou pour éviter un mal,
    il ne faut pas seulement considérer le bien & le mal en soi,
    mais aussi la probabilité qu'il arrive ou n'arrive pas;
    & regarder géometriquement la proportion que toutes ces choses ont ensembles …
    - Antoine Arnauld & Pierre Nicole's (1662, IV, 16) La logique, ou l'art de penser in the original French

    … to judge what one ought to do to obtain a good or avoid an evil,
    one must not only consider the good and the evil in itself,
    but also the probability that it will or will not happen;
    and view geometrically the proportion that all these things have together …
    - Jeffrey's (1981, p. 473) translation

     

     

     

     

    Decision Theory
    1. Decision theory is concerned with the reasoning underlying an agent's choices
    2. Let S denote a set of states s0, s1, etc
    3. Let Φ denote a set of actions φ1, φ2, etc
    4. Let O denote a set of outcomes (or act-state pairs): o11 for the act-state pair φ1-s1, o12 for the act-state pair φ1-s2, etc

    6. In a decision problem, agent X is traditionally confronted with n alternative courses of action, where n ∈ ℕ+
    7. X must determine which of these n alternative courses of action is the most appropriate

    9. Suppose that there are 3 alternative courses of action: φ1, φ2, and φ3
    10. Suppose further that each action, relative to the initial state s0, leads to one and only one successor state
    11. P(s1|φ1) = P(o11|φ1) = 1
    12. P(s2|φ2) = P(o22|φ2) = 1
    13. P(s3|φ3) = P(o33|φ3) = 1
    14. The decision problem reduces to a linear programming problem and agent X will be making decisions under conditions of certainty

    16. Suppose instead that each action is associated with multiple possible outcomes:
      1. φ1 is associated with the outcomes o11 (φ1-s1) and o12 (φ1-s2)
      2. φ2 is associated with the outcomes o23 (φ2-s3) and o24 (φ2-s4)
      3. φ3 is associated with the outcomes o35 (φ3-s5), o36 (φ3-s6), and o37 (φ3-s7)

    18. However, suppose in addition that the conditional probabilities of these outcomes (e.g. P(o11|φ1), P(o12|φ1), P(o23|φ2), etc) are neither known nor estimable
    19. P(o11|φ1) = NA
    20. P(o12|φ1) = NA
    21. P(o23|φ2) = NA
    22. P(o24|φ2) = NA
    23. ⋮    ⋮
    24. Agent X will be making decisions under conditions of uncertainty

    26. Conversely, suppose that these conditional probabilities are either known or can be estimated
    27. P(o11|φ1) = p, where p ∈ (0, 1)
    28. P(o12|φ1) = 1 - p
    29. P(o23|φ2) = q, where q ∈ (0, 1)
    30. P(o24|φ2) = 1 - q
    31. ⋮    ⋮
    32. Here, agent X will be making decisions under conditions of risk
    33. Decision theory is designed to address individual decision-making under conditions of risk

    1. Decision theory is as much a theory about the beliefs and preferences of the agent and how these beliefs and preferences cohere together as it is a theory of choice
    2. The 2 fundamental components of decision theory are:
      1. COMPONENT 1: Beliefs
      2. COMPONENT 2: Preferences

    Set-theoretic notation & Venn diagram representation (part I)

    Set-theoretic notation & Venn diagram representation (part II)
    1. Probability values are used to represent degrees of belief, credence, or confidence
    2. Probability values are in turn constrained by the Kolmogorov axioms

    Kolmogorov Axioms
    AXIOM 1 P(Ω) = 1, where P denotes the probability measure and Ω denotes the sample space
    AXIOM 2 P(A) ≥ 0, where A denotes an outcome
    AXIOM 3 P(A ∪ B) = P(A) + P(B) - P(A ∩ B), where A and B denote distinct outcomes

    NOTE: Where A and B are mutually exclusive:
    P(A ∩ B) = P(∅) = 0
    ∴ P(A ∪ B) = P(A) + P(B)
    RULE 1 (Monotonicity) If A ⊆ B, then P(A) ≤ P(B), where A and B denote distinct outcomes

    Alternatively:
    If B ⊇ A, then P(B) ≥ P(B)
    RULE 2 (Empty set) P(∅) = 0
    RULE 3 (Complement) P(Ā) = P(Ω) - P(A) = 1 - P(A)

    ≻

    Apples are better than lemons relative to the agent's preferences
    (or the agent prefers apples to lemons)




    ∼

    Apples are as good as lemons relative to the agent's preferences
    (or the agent is indifferent between apples and lemons)




    ≺

    Lemons are better than apples relative to the agent's preferences
    (or the agent prefers lemons to apples)
    1. Utility values are used to represent degrees of preference
    2. Utility values are in turn constrained by the von Neumann-Morgenstern axioms
    3. The von Neumann-Morgenstern axioms allow us to assign utility values in a manner that is consistent with the agent's preferences

    Von Neumann-Morgenstern Axioms
    AXIOM 1 (Completeness)
    1. Where A and B denote distinct outcomes, one of the following preference order relations must hold:
    2. RELATION 1: A ≻ B (the agent prefers A to B);
    3. RELATION 2: A ∼ B (the agent is indifferent between A and B); or
    4. RELATION 3: A ≺ B (the agent prefers B to A)

    Alternatively:
    For every A and B, either A ≽ B or A ≼ B
    AXIOM 2 (Transitivity) For every A, B, and C, if A ≽ B and B ≽ C, then A ≽ C
    AXIOM 3 (Independence of Irrelevant Alternatives)
    1. Suppose that A, B, and C are 3 lotteries with A ≽ B
    2. Let t be the probability that a 3rd choice is present: t ∈ [0, 1]
    3. If tA + (1 - t)C ≽ tB + (1 - t)C, then the 3rd choice C is irrelevant
    4. ∴ The preference order relation between A and B (viz. A ≽ B) holds, independently of the presence of C
    AXIOM 4 (Continuity)
    1. Relative to the preference order relation A ≽ B ≽ C:
    2. There exists a probability p such that B is equally as good as pA + (1 - p)C
     

     

    Background image taken from: https://cdn.asiatatler.com/asiatatler/i/th/2020/02/04101225-aurora-1185464-1920_cover_1920x1280.jpg This website has been coded using html, css, and js and is dedicated to B and H .