David H. Wolpert, Santa Fe Institute http://davidwolpert.weebly.com
Nils Bertschinger, Max Planck Institute
Eckehard Olbrich, Max Planck Institute
Juergen Jost, Max Planck Institute, Santa Fe Institute

POSITIVE VALUE OF INFORMATION
Row moves first, and Col receives a noise-swamped
observation of Row’s move before Col moves
h m
H: 0, 0 3, -1
M: -2, 1 2, 3

POSITIVE VALUE OF INFORMATION
Ÿ   Row moves first, and Col receives a noise-swamped
observation of Row’s move before Col moves
h m
H: 0, 0 3, -1
M: -2, 1 2, 3
(Unique) Nash Eq. is (H, h)
Col gets 0

POSITIVE VALUE OF INFORMATION
Ÿ   Row moves first, and Col receives a noise-free
observation of Row’s move before Col moves
h m
H: 0, 0 3, -1
M: -2, 1 2, 3
(Unique) Nash Eq. is (M, m)
Col gets 3

POSITIVE VALUE OF INFORMATION
Ÿ   Row moves first, and Col receives a noise-free
observation of Row’s move before Col moves
h m
H: 0, 0 3, -1
M: -2, 1 2, 3
Getting more information gains 3 for Col

NEGATIVE VALUE OF INFORMATION
Ÿ   Cournot duopoly game (moves are production levels)
Ÿ   Row moves first, and Col receives a noise-swamped
observation of Row’s move before Col moves
h m l
H: 0, 0 12, 8 18, 9
M: 8, 12 16, 16 20, 15
L: 9, 18 15, 20 18, 18

NEGATIVE VALUE OF INFORMATION
Ÿ   Cournot duopoly game (moves are production levels)
Ÿ   Row moves first, and Col receives a noise-swamped
observation of Row’s move before Col moves
h m l
H: 0, 0 12, 8 18, 9
M: 8, 12 16, 16 20, 15
L: 9, 18 15, 20 18, 18
(Unique) Nash Eq. is (M, m)
Col gets 16

NEGATIVE VALUE OF INFORMATION
Ÿ   Cournot duopoly game (moves are production levels)
Ÿ   Row moves first, and Col receives a noise-free
observation of Row’s move before Col moves
h m l
H: 0, 0 12, 8 18, 9
M: 8, 12 16, 16 20, 15
L: 9, 18 15, 20 18, 18
(Unique) Nash Eq. is now (H, l)
Col gets only 9

NEGATIVE VALUE OF INFORMATION
Ÿ   Cournot duopoly game (moves are production levels)
Ÿ   Row moves first, and Col receives a noise-free
observation of Row’s move before Col moves
h m l
H: 0, 0 12, 8 18, 9
M: 8, 12 16, 16 20, 15
L: 9, 18 15, 20 18, 18
Getting more information costs Col 7

NEGATIVE VALUE OF UTILITY
Ÿ   Change a game by imposing a tax on Row for every
move she might make. And she benefits:
L R
T: 10, 10 -2, 8
B: 12, -2 0, 0
Nash eq. is (B, R)
Row gets 0
L R
T: 5, 10 -3, 8
B: 4, -2 -4, 0
Nash eq. is (T, L)
Row gets 5

VALUE OF PARAMETERS OF A GAME
1) How analyze
of a
game?
2) What is relation between a game’s parameters and
the values of those parameters to the players in the game?
3) When is there negative val. of (info., utility) to a player?
4) When is there negative val. of (info., utility) to all players?
5) What are marginal rates of substitution among (values of)
information, utility, etc.?

DIFFERENTIAL VALUE OF INFORMATION
1) “Value of a good” to a consumer is marginal utility of increasing
amount of just that good (per unit good).

DIFFERENTIAL VALUE OF INFORMATION
1) “Value of a good” to a consumer is marginal utility of increasing
amount of just that good (per unit good).
2) Value of a linear combination of goods?
- Marginal utility of increasing amount of just that linear comb .

DIFFERENTIAL VALUE OF INFORMATION
1) “Value of a good” to a consumer is marginal utility of increasing
amount of just that good (per unit good).
2) Value of a linear combination of goods?
- Marginal utility of increasing amount of just that linear comb .
3) Value of arbitrary function f( θ ) of vector of goods θ ?
- Marginal utility of increasing amount of f( θ )
- I.e., directional deriv. of utility along ∇ f( θ ), divided by | ∇ f( θ )| 2

DIFFERENTIAL VALUE OF INFORMATION
1) “Value of a good” to a consumer is marginal utility of increasing
amount of just that good (per unit good).
2) Value of a linear combination of goods?
- Marginal utility of increasing amount of just that linear comb .
3) Value of arbitrary function f( θ ) of vector of goods θ ?
- Marginal utility of increasing amount of f( θ )
- I.e., directional deriv. of utility along ∇ f( θ ), divided by | ∇ f( θ )| 2
4) Value of arbitrary function f of the game parameter θ ?
Directional deriv. of utility along ∇ f( θ ) divided by | ∇ f( θ )| 2

E(u i
)
Θ f
•
•
Θ
•   E(u i
)
•   f

DIFFERENTIAL VALUE OF INFORMATION
Directional deriv. of utility along ∇ f( θ ) divided by | ∇ f( θ )| 2
1) θ c an be noise distribution in an information channel
2) θ c an be rationality parameter in a solution concept
- e.g., exponent in a QRE
3) θ c an be parameter specifying a utility function
4) θ c an be regulator’s choices , e.g., tax rates, regulations, etc.
5) f( θ ) can be mutual information between arbitrary nodes of a MAID
specified by θ , information capacity between them, rationality of a
player (or any other component of θ ), etc.

RESULTS
Theorem : ∇ f( θ ) Ÿ ∇ V( θ ) < | ∇ f( θ )| | ∇ V( θ )|
⇒
∃ changes to θ that reduce f (e.g., a channel’s info capacity) but increase V (e.g., expected utility)
Intuition :
If player values anything in addition to information, a change may reduce their information but increase their expected utility

RESULTS
Theorem : r ( f ) 62 Con ( { r ( V i
) } ) , Con ( { r ( V
⇒
i
) } ) is pointed
∃ changes to θ that decrease f (e.g., a channel’s info. capacity) but increase V (e.g., expected utility) for all players
Intuition : Unless a special condition relates the set of all gradients, there exists a change to game parameter that decreases information but is Pareto-preferred

1) What is relation between a game’s parameters and the values of
information to the players in the game?
2) When is there negative val. of info. to a player?
3) When is there negative val. of info. to all players?
4) What are marginal rates of substitution among (value of)
information, rationality, utility functions, etc.?
Using differential value of information, can answer all these questions - and more

RESULTS
Utility follower
4
3.8
3.6
3.4
3.2
3
2.8
2.6
2.4
2.2
2
0.5
0.45
0.4
0.35
eps
0.3
0.25
0.2
0.15
0.1
0.05
0 100
10
1
0.1
beta
0.01
Follower expected utility plotted against
θ = ( channel noise, QRE exponent ( rationality value ))
Noisy leader-follower binary-move game
- Similar for leader

DIFFERENTIAL VALUE OF INFORMATION
Directional deriv. of utility along ∇ f( θ ) divided by | ∇ f( θ )| 2
Unlike previous approaches, diff. val. of info. is
1) Defined for all edges in an ID
2) Defined for any changes to an edge in a (MA)ID – not just removal
3) Defined separately for each equilibrium in a MAID
4) By evaluating it for multiple f’s, allows us to compare:
•   value of information
•   value of rationality, etc.
“This much extra information is equivalent to this much extra rationality”

DIFFERENTIAL VALUE OF INFORMATION
Directional deriv. of utility along ∇ f( θ ) divided by | ∇ f( θ )| 2
1) Requires choosing a metric tensor over space of game param. θ
2) N.b., θ fixes joint distribution over all variables in the MAID
3) So natural choice for metric is Fisher information metric:
Information geometry of noncooperative games

QUANTIFYING AMOUNT OF INFORMATION
1) Sample P(x) to get an x.
2) Then sample P(y | x) to get a y.
3) How much “information” is in that y, concerning that x?
Gain in accuracy of predicting x using P(x | y) rather than P(x )
4) Formalize this as a difference: log-likelihood of that x, evaluated under P(x | y)
minus log-likel. of that x, mistakenly evaluated under P(x )
5) Average this difference over x and y:
Average amount of information in y concerning x:
Shannon’s mutual information between y and x

RESULTS
@
2 f
@✓ i @✓ j
| r f ( ✓ ) |
| r V ( ✓ ) |
@
2
V
@✓ i @✓ j
| r V ( ✓ ) | 2 r V ( ✓ ) i r V ( ✓ ) j
=
@ 2 f
@✓ i @✓ j
| r f ( ✓ ) |
| r V ( ✓ ) |
@ 2 V
@✓ i @✓ j
1) One of our ECS slides giving a player's mixed strategy as a function of beta and epsilon
2) The associated slide giving expected utility as a function of beta and epsilon
3) 2-player game Edgeworth box plotting functions for only one of the players
(to keep the figure clear). Use this to illustrate our definition of value of info.
4) The associated mutual information and information capacity heat maps.
5) (3) again, to illustrate Prop. 1 visually.
- Formal statement of Prop. 1
6) (3) again, to illustrate Prop. 3 visually.
- Formal statement of Prop. 3
7) Edgeworth box showing functions for both players, illustrating Prop. 4 visually.
Formal statement of Prop. 4

INFLUENCE DIAGRAMS
Game against Nature
- represented as player-fixed Bayes net :
Ÿ Chance Nodes C (pre-fixed CPs)
model Nature, player observations.
Ÿ Decision Nodes D (player fixes
CPs at D, D’, D”) give player’s actions
Ÿ The player sets CP(s) of her
decision nodes based on expected
utility of resultant full Bayes net
C
2
D’’
C
1
D’
C
3
D

PREVIOUS APPROACH TO VALUE OF INFO.
IN GAMES AGAINST NATURE
1) Calculate maximal expected utility with
red edge – U
1 C
1
C
2
D
D’
D’’
C
3

VALUE OF INFORMATION IN GAMES
AGAINST NATURE
1) Calc. maximal expected utility with
red edge – U
1
2) Calc. maximal expected utility without
red edge – U
2
C
2
C
1
D’
D
D’’
C
3

VALUE OF INFORMATION IN GAMES
AGAINST NATURE
1) Calc. maximal expected utility with
red edge – U
1
2) Calc. maximal expected utility without
red edge – U
2
3) “ Value ” of red edge = U
1
- U
2
C
2
C
1
D’
D
D’’
C
3

VALUE OF INFORMATION IN GAMES
AGAINST NATURE
1) Calc. maximal expected utility with
red edge – U
1
2) Calc. maximal expected utility without
red edge – U
2
3) “ Value ” of red edge = U
1
- U
2
4) Cannot measure value of edges into
chance nodes
5) Cannot measure value of changes to
CPs of edges
6) Cannot measure value of nodes
C
2
D’’
C
1
D’
C
3
D

MULTI-AGENT INFLUENCE DIAGRAMS
Multi-Player Game
- represented as player-fixed Bayes net :
Ÿ Chance Nodes C give states,
observations, etc. (fixed CPs)
Ÿ Decision Nodes D i
give i’s actions
(player i sets CP at D i
)
Ÿ Players sets the CP(s) of their
decision nodes based on expected
utility of resultant full Bayes net.
C
2
D
1
C
1
D
2
C
3
D
1

PREVIOUS APPROACH TO VALUE OF
INFO. IN MULTI-PLAYER GAMES
1) Extensive form rep. used (not MAIDs)
2) Provides ordinal “value” of different
information partitions
C
2
C
1
D
2
D
1
D
1
C
3

PREVIOUS APPROACH TO VALUE OF
INFO. IN MULTI-PLAYER GAMES
1) Extensive form rep. used (not MAIDs)
2) Provides ordinal “value” of different
information partitions
3) Cannot use for numeric analysis
4) Difficulties comparing arbitrary
information partitions
5) Cannot apply to games with multiple
equilibria
C
2
D
1
C
1
D
2
C
3
D
1

SHANNON INFORMATION THEORY
1) Very deep and powerful formalization of information
2) Arises in earlier work on games, e.g., as
•   Tool to formalize modeler’s uncertainty
•   Model of bounded rational players
- Quantal Response Equilibrum – QRE
•   Analogy with statistical physics
•   Total payoff in multi-stage games with multiplicative payoffs
3) Never used before to quantify information within the game
(e.g., as mutual information, information capacity, etc.)

SHANNON INFORMATION THEORY
How use Shannon information theory to analyze value of information in games?

SHANNON INFORMATION THEORY
How use Shannon information theory to analyze value of information in games?
Can we answer our motivating questions if we figure out how to answer this one?

SHANNON INFORMATION THEORY
How use Shannon information theory to analyze value of information in games?
Can we answer our motivating questions if we figure out how to answer this one?
Yes

MUST BE MORE SPECIFIC ….
•   Value of information
•   { Marginal } value of information
•   Marginal value of information { capacity between v
1
and v
2
} or
•   Marginal value of { mutual } information { between v
1
and v
2
} or
•   Marginal value of { multi} information { among v
1
, v
2
, v
3
, … }
…
•   Marginal value of mutual information between v
{ to player i }
1
and v
2

ROADMAP
1) Graphical models of games
2) Previous work involving
Shannon Info. Theory and games
3) From marginal utility to value of information; information geometry of games
4) Theorems and plots

NEGATIVE VALUE OF INFO. TO ALL PLAYERS
Braess’ paradox:
Ÿ t = commute time for each player on indicated road,
Ÿ T = total number of players on indicated road,
Ÿ 4000 players total,
Ÿ cost = 4 for a player to try to go from A to B,
Ÿ A priori probability that road from A to B is open = .1
Ÿ All players get noise-swamped signal of whether A-to-B is open
Nash Eq : 2000 players go from Start to A to End,
2000 players go from Start to B to End.
All players have 65 minute commute

NEGATIVE VALUE OF INFO. TO ALL PLAYERS
Braess’ paradox :
Ÿ t = commute time for each player on indicated road,
Ÿ T = total number of players on indicated road,
Ÿ 4000 players total,
Ÿ cost = 4 for a player to try to go from A to B,
Ÿ A prior i probability that road from A to B is open = .1
Ÿ All players get noise-free signal of whether A-to-B is open
Nash Eq : Same as before when A-to-B is closed (.9 probability)
When A-to-B is open, all players go from Start to A to B to End, in which case all players have 84 minute commute

NEGATIVE VALUE OF INFO. TO ALL PLAYERS
Braess’ paradox :
Ÿ t = commute time for each player on indicated road,
Ÿ T = total number of players on indicated road,
Ÿ 4000 players total,
Ÿ cost = 5 for a player to try to go from A to B,
Ÿ A prior i probability that road from A to B is open = .1
Ÿ All players get noise-free signal of whether A-to-B is open
When all players get extra information (is A to B open?), either no change in commute time (.9 probability), or all players have 19 minute longer commute (.1 probability)

VALUE OF INFORMATION
1) What is relation between a game’s parameters and
the
to the players in the game?
2) When is there negative val. of info. to a player?
3) When is there negative val. of info. to all players?

VALUE OF … ANYTHING?
1) What is relation between a game’s parameters and
the values of information to the players in the game?
2) When is there negative val. of info. to a player?
3) When is there negative val. of info. to all players?
Can we analyze value of tax, regulation, etc., with the same formalism?

DIFFERENTIAL VALUE OF INFORMATION
Ÿ   Row moves first, and Col receives a noisy
observation of Row’s move before Col moves
h m
H: 0, 0 3, -1
M: -2, 1 2, 3

RESULTS
Leader strategy
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.5
0.45
0.4
0.35
eps
0.3
0.25
0.2
0.15
0.1
0.05
0 100
10
1
0.1
beta
0.01
Leader mixed strategy plotted against
θ = ( channel noise, QRE exponent ( rationality value ))
Noisy leader-follower binary-move game
- Similar for follower

RESULTS
0.5
0.45
0.4
0.35
0.3
0.25
0.2
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
0.15
0.1
0.05
0
0 0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
5
4.5
4
3.5
3
2.5
2
Theorem : ∇ f( θ ) Ÿ ∇ V( θ ) < | ∇ f( θ )| | ∇ V( θ )|
⇒
∃ changes to θ that reduce f (e.g., a channel’s info capacity) but increase V (e.g., expected utility)

RESULTS
0.5
0.45
0.4
0.35
0.3
0.25
0.2
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
0.15
0.1
0.05
0
0 0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
5
4.5
4
3.5
3
2.5
2
Intuition :
If player values anything in addition to information, a change may reduce their information but increase their expected utility

RESULTS
0.5
0.45
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
5
4.5
0.4
0.35
4
0.3
0.25
0.2
3.5
3
0.15
0.1
2.5
0.05
0
0
2
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
Theorem : r ( f ) 62 Con ( { r ( V i
) } ) , Con ( { r ( V
⇒
i
) } ) is pointed
∃ changes to θ that decrease f (e.g., a channel’s info. capacity) but increase V (e.g., expected utility) for all players

RESULTS
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
5
4.5
4
3.5
3
2.5
2
Intuition : Unless a special condition relates the set of all gradients, there exists a change to game parameter that decreases information but is Pareto-preferred

RESULTS
0.5
0.45
0.4
0.35
0.3
0.25
0.2
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
0.15
0.1
0.05
0
0 0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
5
4.5
4
3.5
3
2.5
2
Level surfaces of expected utilities and channel capacity plotted against channel parameters in noisy leader-follower binary-move game.

RESULTS
0.5
0.45
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
5
4.5
0.4
0.35
4
0.3
0.25
0.2
3.5
3
0.15
0.1
2.5
0.05
0
0
2
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
Theorem :
@
2 f
@✓ i @✓ j
| r f ( ✓ ) |
| r V ( ✓ ) |
@
2
V
@✓ i @✓ j @ 2 f | r f ( ✓ ) | @ 2 V
| r V ( ✓ ) | 2 r V ( ✓ ) i r V ( ✓ ) j
=
@✓ i @✓ j | r V ( ✓ ) | @✓ i @✓ j
⇒
The set { θ : no changes to θ reduce f while increasing V} has measure 0

RESULTS
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
5
4.5
4
3.5
3
2.5
2
Intuition : Unless a special condition between Hessians and gradients holds for all values of game parameter θ , there exists a game parameter θ and changes to it that decrease information but increase expected utility

RESULTS
0.5
0.45
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
5
4.5
0.4
0.35
4
0.3
0.25
0.2
3.5
3
0.15
0.1
2.5
0.05
0
0
2
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
Theorem : r ( f ) 62 Con ( { r ( V i
) } ) , Con ( { r ( V i
) } ) is pointed
⇒
∃ changes to θ that increase f (e.g., a channel’s info. capacity) but increase V (e.g., expected utility) for all players

RESULTS
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
5
4.5
4
3.5
3
2.5
2
Intuition : Unless a special condition relates the set of all gradients, there exists a change to game parameter that increases information and is Pareto-preferred

RESULTS
0.5
0.45
Branch 4: CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
UTIL-LEADER
UTIL-FOLLOWER
CHANNEL-CAPACITY
5
4.5
0.4
0.35
4
0.3
0.25
0.2
3.5
3
0.15
0.1
2.5
0.05
0
0
2
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
eps1
•   Issue is Pareto-improving change to θ , not
Pareto-improving change to mixed strategy profile
•   Not uncommon that the “special condition” does hold.
Example : constant-sum games .
•   Generically, if number players < dim( θ ), ∃ both changes to θ that: i) Increase f and are Pareto-improving ii) Decrease f and are Pareto-improving.