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Lecture 3: quantum probabilities and bounded rationality

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Title: Lecture 3: quantum probabilities and bounded rationality


1
Lecture 3quantum probabilities and bounded
rationality
reinhard blutner http//www.blutner.de blutner_at_uva
.nl
Institute for Logic,Language and Computation
2
Outlook
  • Quantum probabilities and Gleasons theorem
  • Bounded rationality
  • The conjunction puzzle
  • The disjunction puzzle
  • Other puzzles

3
1 Quantum probabilities and Gleasons theorem
4
Classical and non-classical observables
  • Classical observables are defined by commuting
    operators, non-classical ones by non-commuting
  • Typical but unacceptable claim The macro-world
    can always be described by classical observables
    (and the micro-world by non-classical, quantum
    observables)
  • The emergence of quantal macrostates does not
    necessarily require the reference to
    corresponding quantal microstates (Aerts)
  • Complementary observables can arise in classical
    dynamic systems with incompatible partitions of
    the phase space (b. Graben Atmanspacher)

5
Qantum probabilities
  • Let H be a Hilbert space. Define an additive
    probability measure on a projection lattice of H,
    i.e.
  • ?(ab) ?(a) ?(b) if a and b are orthogonal
    projections
  • ?(1) 1 (1 projects the whole Hilbert space H)

6
Gleasons theorem
  • Let H have dimension gt 2. Then every countably
    additive probability measure on a projection
    lattice of H has a unique linear extension
    defined for all (bounded) operators on H.

7
Consequence of Gleasons theorem
  • Each additive probability measure ? on a
    projection lattice of H (dimension gt 2) can be
    represented by a density operator
  • ? ?j m(j) j??j
  • on H in the following way
  • ?(a)  Tr(?a) 1
  • supposed the vectors j? form an orthonormal
    system of eigenvectors of a, and ?j m(j) 1
  • 1 Tr(X) ?i ?i X i? (for an orthonormal
    system of eigenstates i of X)

8
Proof
  • ?(a)  ?(?ij i??iaj??j)
  • ?(?i i??i) ?iai? (since aj? ?j j?)
  • ?i ?(i??i) ?iai?
  • ?i m(i) ?iai?
  • ?ij m(i) ?ij??jai?
  • ?i ?i ?j(m(j)j??j) a i?
  • Tr(?a)

9
Conditioned probabilities1
  • For defining conditioned probabilities we
    stipulate the following condition for all
    projections a, b in H
  • ?(ba) ?(b)/?(a) if b ? a (i.e. ba b), ?(a)
    ? o
  • Fact ?(ba) ?(abaa) ?(aba)/?(a)
  • Proof b aba a'ba aba' a'ba'
  • ?(ba) ?(abaa) ?(a'baa) ?(aba'a)
    ?(a'ba'a)
  • ?(a'baa) ?(aba'a) ?(a'ba'a) 0
  • (e.g. ?(a'baa) ? ?(a'a) 0, etc. )
  • Thus, ?(ba) ?(abaa) ?(aba)/?(a)

1 I follow the very elegant formulation given by
Gerd Nistegge (2008) in annals of physics
10
Asymmetric conjunction
  • Use the notion (a b) for a sequence of two
    projection operators
  • The definition for the probability for sequences
    is as follows ?(a b) ? ?(a) ?(ba) ?(aba)
  • If a and b commute we get ?(a b) ?(b a),
    i.e. ?(a) ?(ba) ?(b) ?(ab) (Bayesian
    formula)
  • If a and b dont commute the Bayesian formula can
    be violated!

11
Conditioning
  • Theorem Let a and b be projection operators,
    then 1
  • ?(b) ?(a b) ?(a' b) ?(aba' a'ba)
  • Proof a a' b b' 1 aa' bb' 0
  • b aba a'ba aba' a'ba'
  • Using Gleasons theorem yields the result!
  • Remark The term ?(aba' a'ba) describes an
    interference effect which vanishes in the
    classical case (where all operators are
    commuting).
  • 1 a' means the same as a?

12
Explicit calculation of the interference term
  • Assume that a and a' represent pure states a
    a??a, a' a'??a', aa' 0, aa' 1.
  • Fact ?(aba' a'ba) 2 ?1/2(a b) ?1/2 (a' b)
    cos(?)
  • Proof ?(aba' a'ba)
  • ?ab??a'b? a??a' ?ab??a'b? a'??a
  • use ?ab??a'b? ?ab??a'b? exp(i?)
  • 2?ab??a'b??((a??a' exp(i?) a'??a
    exp(-i?))
  • 2 ?1/2(a b) ?1/2 (a' b) cos(?)

13
2 Bounded rationality
14
Herbert Simon 1955
  • Boundedly rational agents experience limits in
    formulating and solving complex problems and in
    processing (receiving, storing, retrieving,
    transmitting) information.
  • There is a number of dimensions along which
    "classical" models of rationality can be made
    more realistic without giving up rigorous
    formalization.
  • limiting what sorts of utility functions there
    might be
  • recognizing the costs of gathering and processing
    information
  • the possibility of having a "multi-valued"
    utility function.

15
Hard problems for bounded rationality
  • Disjunction effects
  • Conjunction effects
  • Ellsberg paradox
  • Allais paradox
  • Framing effects

16
2.1 Disjunction Effects
17
Savages sure-thing principle
  • A businessman contemplates buying a certain
    piece of property. He considers the outcome of
    the next presidential election relevant to the
    attractiveness of the purchase. So, to clarify
    the matter for himself, he asks whether he would
    buy if he knew that the Republican candidate were
    going to win, and decides that he would do so.
    Similarly, he considers whether he would buy if
    he knew that the Democratic candidate were going
    to win, and again finds that he would do so.
    Seeing that he would buy in either event, he
    decides that he should buy, even though he does
    not know which event obtains. (Savage, 1954, p.
    21)

18
Tversky and Shafir
  • B A p
  • B ?A q
  • B (A ? ?A) between p q
  • Tversky and Shafir (1992) show that
    significantly more students report they would
    purchase a nonrefundable Hawaiian vacation if
    they were to know that they have passed or failed
    an important exam than report they would purchase
    if they were not to know the outcome of the exam.
  • Disjunction effect ?(B) ? ?(BA) ?(A)
    ?(B?A) ? (?A)

19
Khrennikov
Test A Test B
20
Disjunction effect
  • Subjects have to decide whether the two objects
    in Test A (Test B) are of the same size or not
  • Test A and Test B are realized separately and in
    turn (first A then B after 2 seconds)
  • ?(B) ? ?(BA) ?(A) ?(B?A) ? (?A)

21
Results
  • ?(B) 0.45 ?(? B) 0.55
  • ?(A) 0.7 ?(? A) 0.3
  • ?(B/A) 0.43 ?(? B/A) 0.57
  • ?(B/?A) 1 ?(?B/?A) 0
  • ?(B) ? ?(BA) ?(A) ?(B?A) ? (?A) 0.15
    (sign.)

22
Disjunction effect interference effect
  • ?(b) ?(ab) ?(a'b) ?
  • ? 2 ?1/2(ab) ?1/2 (a'b) cos(?)
  • Note The phase factors ? correspond to the
    expressions ?ab??a'b? ?ab??a'b? ?
    exp(i ?)
  • ?(b) ? ?(a b) ?(a b') ?
  • 2?0.3? cos(?) 0.15
  • ? cos(?) 0.25 ? ? 75,5?

23
2.2 Conjunction Effects
24
Tversky Kahnemann (1983)
  • Linda is 31 years old, single, outspoken and
    very bright. She majored in philosophy. As a
    student, she was deeply concerned with issues of
    discrimination and social justice, and also
    participated in anti-nuclear demonstrations.
  • Linda is a teacher in elementary school.
  • Linda works in a bookstore and takes Yoga
    classes.
  • Linda is active in the feminist movement.
    (F) (6,1)
  • Linda is a psychiatric social worker.
  • Linda is a member of the League of Women Voters.
  • Linda is a bank teller. (T) (3,8)
  • Linda is an insurance salesperson.
  • Linda is a bank teller and is active in the
    feminist movement. (TF) (5,1)

25
Different experimental procedures
  • Probability judgments
  • ranking probabilities of the different evens
  • probability assessment on a 9 point scale
  • Frequency judgments
  • Judging representativeness
  • degree to which an event is representative of an
    appropriate mental model
  • degree of correspondence between an instance and
    a category, an outcome and a model
  • Degree of prototypicality

26
Asymmetric conjunction resolves the conjunction
puzzle in the probabilistic case
  • ?(b) ?(ab) ?(a'b) ?
  • ? 2 ?1/2(ab) ?1/2 (a'b) cos(?)
  • Conjunction effect ?(ab) ? ?(b) ??(a'b) ? 2
    ?1/2(ab) ?1/2 (a'b) cos(?)
  • Example for ?? ?(ab) ? ?(b) ?1/2 (a'b)
    ?1/2 (a'b) 2 ?1/2(ab)

27
Example
  • ?(b) 0.38 (Linda is a bank teller)
  • ?(a) 0.61 (Linda is a feminist)
  • ?(a b) 0.51 (Linda is a feminist bank teller)
  • Conjunction effect 0.13
  • ?(ba) 0.84, ?(a' b) 0.71
  • 0.13 ?1.2 cos(?) ? 0.71,
  • i.e. cos(?) -0.7, ? 2.35 ? 270?

28
2.3 Ellsberg Paradox
29
Ellsberg Paradox
  • Urn containing 30 red balls 60 balls you
    cannot really see, black or yellow proportion
    unknown.
  • Game A you receive 100 if you draw a red ball
  • Game B you receive 100 if you draw a black ball
  • Most people prefer A ? B
  • Game C you receive 100 if you draw a red or
    yellow ball
  • Game D you receive 100 if you draw a black or
    yellow ball
  • Most people prefer D ? C

30
Expected utility theory
  • Preferring A over B means that the expected
    utilty for A is higher than the expected utility
    for B
  • A ? B
  • ?(R)U(100)(1- ?(R))U(0) gt ?(B)U(100)(1-
    ?(B))U(0)
  • i.e. ?(R) gt ?(B) assuming U(100) gt U(0)
  • D ? C
  • ?(B)U(100)?(Y)U(100)?(R)U(0) gt
    ?(R)U(100)?(Y)U(100)?(B)U(0)
  • i.e. ?(B) gt ?(R) assuming U(100) gt U(0)
  • This contradiction indicates that the preferences
    are inconsistent with expected-utility theory

31
Sure thing principle
In formal terms, the principle states that the
choice between two actions is unaffected by the
pay-offs in a constant column
32
2.4 Allais paradox
33
A choice problem
  • The choice problem designed by Maurice Allais
    shows an inconsistency of actual observed choices
    with the predictions of expected utility theory
  • The problem arises when comparing participants'
    choices in two different experiments, each of
    which consists of a choice between two gambles, A
    and B.
  • The particular payoffs and chances are essential

34
Example
1 U(1M) gt 0.89 U(1M) o.o1 U(0) 0.1
U(5M)
0.89 U(0) 0.11 U(1M) lt 0.9 U(0) 0.1
U(5M) i.e. 0.11 U(1M)lt o.o1 U(0) 0.1
U(5M)
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