On the Dynamics of Machine Learning Algorithms and Behavioral Game Theory

On the Dynamics of Machine Learning
Algorithms and Behavioral Game Theory
Towards Effective Decision Making
in Multi-Agent Environment
Graduate School of Systems and Information Engineering
University of Tsukuba
Sep 17, 2016
Rikiya Takahashi, Ph.D. (SmartNews, Inc.)
rikiya.takahashi@smartnews.com

About Myself
●
Rikiya TAKAHASHI ( 高橋力矢 )
● Engineer in SmartNews, Inc., from 2015 to current
● Research Staff Member in IBM Research – Tokyo, from 2004 to 2015
● Ph.D in Engineering from University of Tsukuba, 2014
– Dissertation: "Stable Fitting of Nonparametric Models to Predict Complex Human
Behaviors"
– Supervisor: Prof. Setsuya Kurahashi
● M.Sc (2004) & B.Eng (2002) from The University of Tokyo
● Research Interests: machine learning, reinforcement learning,
cognitive science, behavioral economics, complex systems
● Descriptive models about real human behavior
● Prescriptive decision making by exploiting such descriptive models

References
Choice and Social Interaction
Why did you purchase Windows 10 XXX Edition?
Because the price and quality of that OS were good?
Or because your friends were using it?
Or both reasons?
Are you interested in quantifying each factor for better
decision making?
SmartNews Connecting Machine Learning Algorithms with Behavioral Game Theo

References
Decision Making under Social Uncertainty
You can be either a player or a designer of the market.
Players: consumers, ﬁrms competing with other brands
Designers: politicians, platformer of auction or SNS
In both scenarios you must optimize your decisions under
uncertainty over other players’ decisions.

0 5 10 15 20 25 30 35
0
0.25
0.5
0.75
1
Elapsed Time [days]
Retention
What I was doing during PhD:
Stable Fitting of Power Law Models
● Heavy-tail distributions / long-range dependence
● Bounded rationality: incomplete information, cognitive bias
● Positive feedback: richer-get-richer, increasing survival prob.
Travel time in road network Ebbinghaus forgetting curve
Asset returns in finance Pageview of a video in YouTube
Power-law decay
by cascading word-
of-mouth (Crane &
Sornette, 2008)
Heavy-tail by
price crash
(stimultaneous
shorting)
http://finance.yahoo.com
Heavy-tail
by huge
traffic
congestion
http://en.wikipedia.org/
wiki/Traffic_congestion
Power-law
decay by
interaction
among short-,
mid-, & long-
term memories

What I was doing during PhD: Power-Law
Models = Multi-Scale Nonparametrics
● Global optimization in fitting nonparametric models
● Non-linear modeling by linearly mixing
local or multi-scale basis functions
● Convex optimization of the mixing weights
● Domain-specific design of fixed basis functions
ElapsedTime
Retention
Value
ProbabilityDensity
Heavy-tail distribution as
scale-mixture of Gaussians
Power-law decay as scale-
mixture of exponential decays

Agenda
● Irrationality and Disequilibrium: essential
phenomena making social science challenging
● Failed Forecasts by Rational Economic Models:
Irrational Disequilibrium or Multiple Equilibria?
● Frontiers in Mathematical Modeling of
Irrationality: a transitional-state perspective
● For on-going PhD students: how to exploit your
research experiences into Jobs

Intertemporal Decision Making
● $100 today = $100 * (1+interest rate) in the future
● Objective must be time-consistent.
● Exponential discounting (constant interest rate)
● Unchanged preference order over time
V (t0)=V (t)exp(−λ (t−t0)) where t> t0, λ > 0

Real Human Discounts by Power Law
● Hyperbolic discounting (Ainslie, 1974)
● Time-inconsistent preference order
V (t0)=
V (t)
(1+ λ (t−t0))α where t> t0, λ > 0,α > 0

Irrationality of Hyperbolic Discounting
● Discrepancy between thought and action
● Long-term-oriented when the decision time is distant.
● But suddently become myopic as the time reaches.
"About 1 month ago, I was thinking I would study hard (=long-
term large utility) in the last 1 week before the exam, but I did
play video games (=short-term small utility) this week..."
Long-term Option B is more prioritized
than Short-term Option A at t=0,
but its order is reversed at t=2.

Irrationality of Hyperbolic Discounting
● Money pumps (Cubit and Sugden, 2001)
● We can steal money from hyperbolic discounter without
risks, while cannot steal from exponential discounter.
At time t=0, we borrow Option B and
exchange it with the target's Option A
and $2.5 (=$15-$12.5).
Then we earn interests on this $2.5.
At time t=3, we exchange our Option A
with the target's Option B and $10
(=$30-$20). Then return this Option B.
We get ($2.5 * (1 + interests) + $10 –
borrowing cost) without risks.

Known Counterarguments
● Hyperbolic discounting is rather rational, when the
interest rates in the future is uncertain.
● E.g., (Azfar, 1999; Farmer & Geanakoplos, 2009)
● Meaningful particularly in financial decision making
● Integral on gamma prior distribution for interest rate
(multi-scale mixture of exponential discountings)

Power-Law and Disequilibrium
● Power-law or fat tails in asset-return distributions
● See (Cont, 2001) for stylized facts.
● Short-term momentum generates outlying returns
● Positive autocorrelation in rare events (Sornette, 2004).
http://www.proba.jussieu.fr/pageperso/ramacont/papers/empirical.pdf

What Causes Fat Tails?
● Hypothesis #1. Interplay among momentum traders
● E.g., Log-Periodic Power-Law (LPPL) model (Johansen+,
1999) as an extension of rational bubble model (Blanchard &
Watson, 1982)
Sell
?
Sell
!
Sell More!!
$$: market price
http://arxiv.org/pdf/1107.3171.pdf
si=sign(K ∑j∈N (i)
sj+ ε i)
si∈{−1,+ 1}
K: strength of interaction
N(i): set of neighbors for investor i
epsiloni : investor i's own indiosyncratic
prediction

● Hypothesis #2. Over-confidence on stability
● Leverage in low-volatility period (Thurner+, 2012)
– Once a downward price fluctuation occurs, resulting
margin call causes rushes of selling into an already
falling market, amplifying the downward price movement.
Low-Volatility Period
with Leverage
Sudden Price Drop
with Margin Calls

● Implications are obtained by explicitly modeling
and simulating the dynamics in trading.
● Physical modeling using stochastic processes
● Transitionary states and disequilibrium play crucial roles.
● Do not think that the system is always in equilibrium.
https://www.amazon.co.jp/dp/B009IRP3GW
M. Buchanan, “Forecast: What Physics, Meteorology,
and the Natural Sciences Can Teach Us About
Economics,” A&C Black, 2013

Regarding Irrationality as Disequilibrium
● Assume that human plays a game in his mind.
● Then irrationality is regarded as an outcome
from state dynamics in mental processes.
● Rationality = choose the strategy in stationary state
● Irrationality = choose a strategy in transitional state
● Possibility to formalize many social phenomena
universally via explicit state dynamics
● For better understanding: play p-beauty contest

References
Understand Dynamics by (2/3)-beauty Contest
What are the numbers chosen by these n players?
Each player i ∈{1, . . ., n} chooses an integer Yi ∈[0, 100].
Winner(s): player(s) whose Yi is closest to 2
3
1
n
n
j=1Yj .

References
Equilibrium of p-beauty Contest (Moulin, 1986)
Nash Equilibrium when 0 ≤ p < 1: ∀i Yi = 0
1 Let C0 be a set of purely na¨ıve players, who choose from
0 to 100 at uniformly random.
2 Since E[ 1
|C0| i∈C0
Yi ] = 50, a slightly more strategic
player in class C1 will choose round(50 × 2/3)=33.
3 Further strategic players in class C2 will choose
round(33 × 2/3)=22. Players in class C3 will choose ...
At convergence, every player should choose zero.
However, do you believe such prediction?

References
A Result of p-beauty Contest by Real Humans
Mean is apart from 0 (Camerer et al., 2004; Ho et al., 2006).
Table: Average Choice in (2/3)-beauty Contests
Subject Pool Group Size Sample Size Mean[Yi ]
Caltech Board 73 73 49.4
80 year olds 33 33 37.0
High School Students 20-32 52 32.5
Economics PhDs 16 16 27.4
Portfolio Managers 26 26 24.3
Caltech Students 3 24 21.5
Game Theorists 27-54 136 19.1

References
Unreality of Nash Equilibrium
Every player is homogeneous.
All of them adopt the same thinking process.
Every player has inﬁnite forecasting horizon.
Can all real humans think so intelligently?
Such unrealistic assumption leads vulnerability to perturbation.
What if one player does not understand the game rule?
What if one player intends to punish “rational” others?

Analyzing More Complex Interactions
● Agent-based modeling
● Can be free from some assumptions: homogeneity,
complete information, rationality, etc.
● True challenge: design of good agent models
● Often too many degrees of freedom in tuning
J. M. Epstein, “Generative Social Science:
Studies in Agent-Based Computational
Modeling,“ Princeton University Press, 2012.
S. F. Railsback and V. Grimm, “Agent-Based
and Individual-Based Modeling: A Practical
Introduction,“ Princeton University Press, 2011.

One Viewpoint for Good Agent Design
● Explicitly model human's bounded rationality.
● Irrationality is not the outcome of human's stupidity.
● Human does try optimization, but cannot reach the
true optimum due to the lack of mental resources.
– Finite memory about past events
– Uncertainty over the future environment
– Uncertainty over other agents' decisions
● Refer to Behavioral Game Theory
● Jewels in modeling bounded-rational agents

Short Summary
● Discussed irrationality in the real world.
● Observed that transitional states are often more
realistic forecasts than equilibrium.
● Discussed direction for good agent models:
hints for accurately modeling dynamics.

What is Rational / Irrational?
● Rationality = optimizing a consistent objective
● Irrationality = any behavior different from rationality
● Inconsistent optimization risks being manipulated by others.
● E.g., hyperbolic discounting: time-inconsistent preference order
causes vulnerability of money pumps.
● Other forms of irrational decision making
● Choice from options whose coverage is manipulated by others

References
Discrete Choice Modelling
Goal: predict prob. of choosing an option from a choice set.
Why solving this problem?
For business: brand positioning among competitors
For business: sales promotion (yet involving some abuse)
To deeply understand how human makes decisions

References
Random Utility Theory as Rational Model
Each human is a maximizer of a probabilistic utility.
i’s choice from Si = arg max
j∈Si
fi (vj )
mean utility
+ εij
random noise
Si : choice set for i, vj : vector of j’s attributes, fi : i’s
mean utility function
Assuming independence among every option’s attractiveness
For both mean and noise: (e.g., logit (McFadden, 1980))
For only mean: (e.g., nested logit (Williams, 1977))

References
Context Eﬀects: Complexity of Human’s Choice
An example of choosing PC (Kivetz et al., 2004)
Each subject chooses 1 option from a choice set
A B C D E
CPU [MHz] 250 300 350 400 450
Mem. [MB] 192 160 128 96 64
Choice Set #subjects
{A, B, C} 36:176:144
{B, C, D} 56:177:115
{C, D, E} 94:181:109
Can random utility theory still explain the preference reversals?
B C or C B?

References
Similarity Eﬀect (Tversky, 1972)
Top-share choice can change due to correlated utilities.
E.g., one color from {Blue, Red} or {Violet, Blue, Red}?

References
Attraction Eﬀect (Huber et al., 1982)
Introduction of an absolutely-inferior option A−
(=decoy)
causes irregular increase of option A’s attractiveness.
Despite the natural guess that decoy never aﬀects the choice.
If D A, then D A A−
.
If A D, then A is superior to both A−
and D.

References
Compromise Eﬀect (Simonson, 1989)
Moderate options within each chosen set are preferred.
Diﬀerent from non-linear utility function involving
diminishing returns (e.g.,
√
inexpensiveness+
√
quality).

Multiple Equilibria also Spoil Forecasts
● Pivotal mechanism (Clarke, 1971) to decide
whether to start a public project
● Every player discloses a utility of the project outcome.
● If and only if sum(utilities) > 0, then project is started.
● Player i must pay tax amount abs(other players' utility sum), when
sign of player i's utility and that of other players is opposite.
● For every player, honestly disclosing his true utility
is optimal regardless other players' utilities.
sum(utilities) = 1
decision = start
disclosed utility -1 2 5 -3 -2
tax 2 0 0 4 3

Multiple Equilibria also Spoil Forecasts
● Failure of pivotal mechanism (Attiyeh+, 2000)
● Being rational is difficult because of too complex rules
● Even if rationality leading into an equilibrium exists,
which equilibrium will be actually chosen?
● Each equilibrium has its own path from initial state.
● Identifying both of the path and finite time is hard.
● One promising way: converting transitional state in one
game into an equilbrium of other game.

Short Summary
● Introduced more examples of irrational decision
making by real humans.
● Irrationality spoils forecasting by standard
economic models.
● Multiple equilibria further complicate the
forecasting in addition to the irrational
disequilibrium.

References
Game with Heterogeneous Pay-Offs
Which numbers will be chosen by these 3 players?
Each player i ∈{1, . . ., n} chooses an integer Yi ∈[0, 10].
Player #1’s pay-off: 39 + 12Y1 − (Y1+Y2)2
Player #2’s pay-off: 47 + 20Y2 − (Y2+Y3)2
Player #3’s pay-off: 6Y3 − (Y3− 1
2
(Y1 + Y2))2

References
An Idiot’s View of Game Theory
If other players’ decisions Y i (Y1, . . . , Yi−1, Yi+1, . . . , Yn)
are known, optimal decision Y ∗
i for player i is given by
∀i ∈{1, . . . , n} Y ∗
i |Y i = arg max
Y
ui (Y , Y i ). (1)
ui : utility function of player i
Game theory is merely solving a system of n equations by
assuming ∀i Yi ≡ Y ∗
i in Eq. (1).
Every player is assumed to be a utility maximizer.
Variety of games just comes from the variable type of Yi .
However, what if players are irrational or unpredictable?

References
Equilibrium of Linearly-Solvable Games
Maximization of concave-quadratic equation = linear equality
0 = 12 − 2(Y ∗
1 +Y2)
0 = 20 − 2(Y ∗
2 +Y3)
0 = 6 − 2(Y ∗
3 −
1
2
(Y1 + Y2))
∀i Yi ≡Y ∗
i leads a matrix-vector relationship


2 2 0
0 2 2
−1 −1 2




Y ∗
1
Y ∗
2
Y ∗
3

 =


12
20
6

 .
(Y ∗
1 , Y ∗
2 , Y ∗
3 ) = (2, 4, 6) with Pay-oﬀs = (27, 27, 27)

References
Belief Learning: Iterative Solving of Game
Equilibrium is tractable only for limited classes of utility
functions, while in general is iteratively computed as
t =0: Initialize each player’s decision by some value.
t > 0: Compute the t-step optimum given the
(t−1)-step decisions by others.
∀i ∈{1, . . . , n} Y
(t)
i |Y
(t−1)
i = arg max
Y
ui (Y , Y
(t−1)
i )
Belief learning: classes of algorithms to iteratively compute
the equilibrium. (t + 1)-step looking-ahead player beats the
t-step-only players, (t + 2)-step player beats...
How about using Y
(t)
i at ﬁnite t, instead of the one at t →∞?

How to Formalize Context Effects?
● What dynamics causes context effects?
● Hypothesis: a dynamical process to estimate utility
function (Takahashi & Morimura, 2015).
● Irrational contextual effects are observed via
regularized estimates of the utility function.
● Machine learning as a dynamical process
● Transitionary state in maximum-likelihood estimation
● Stationary state in Bayesian shrinkage estimation

References
Gaussian Process Uncertainty Aversion (GPUA)
A dual-personality model regarding utilities as samples in
statistics (Takahashi and Morimura, 2015)
Assumption 1: Utility function is partially disclosed to DMS.
1 UC computes the sample value of every option’s utility,
and sends only these samples to DMS.
2 DMS statistically estimates the utility function.

References
GPUA: Mental Conflict as Bayesian Shrinkage
Assumption 2: DMS does Bayesian shrinkage estimation.
i ∈{1, . . . , n}: context, yi ∈{1, . . . , m[i]}: final choice
Xi (xi1 ∈RdX , . . . , xim[i]) : features of m[i] options
Objective Data: values of random utilities
vi (vi1, . . . , vim[i]) ∼N µi , σ2
Im[i] , vij = b+wφ φ (xij )
µi : Rm[i]: vec. of the true mean utility, σ2: noise level
b: bias term, φ : RdX →Rdφ : mapping function. wφ: vec. of coefficients
Subjective Prior: choice-set-dependent Gaussian process
µi ∼ N 0m[i], σ2
K(Xi ) s.t. K(Xi ) = (K(xij , xij ))∈Rm[i]×m[i]
µi ∈Rm[i]: vec. of random utilities, K(·, ·): similarity between options
Final choice: based on (Posterior mean u∗
i + i.i.d. noise) as
u∗
i = K(Xi ) Im[i]+K(Xi )
−1
b1m[i]+Φi wφ ,
yi = arg max
j
(u∗
ij + εij ) where ∀j εij ∼ Gumbel.

References
GPUA: Irrationality by Bayesian Shrinkage
Implication of (2): similarity-dependent discounting
u∗
i = K(Xi ) Im[i]+K(Xi )
−1
shrinkage factor
b1m[i]+Φi wφ
vec. of utility samples
. (2)
Under RBF kernel K(x, x ) = exp(−γ x − x 2
),
an option dissimilar to others involves high uncertainty.
Strongly shrunk into prior mean 0.
Context eﬀects as Bayesian uncertainty aversion
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4
FinalEvaluation
X1=(5-X2)
DA
-
A
{A,D}
{A,A-
,D}
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4FinalEvaluation
X1=(5-X2)
DCBA
{A,B,C}
{B,C,D}

References
GPUA: Convex Optimization using Posterior Mean
Global ﬁtting of the parameters using data (Xi , yi )n
i=1
Fix the mapping and similarity functions during updates.
Shrinkage factor Hi K(Xi )(Im[i] + K(Xi ))−1
is
constant!
Obtaining a MAP estimate is convex w.r.t. (b, wφ).
max
b,wφ
n
i=1
( bHi 1m[i]+Hi Φi wφ
Context−speciﬁc Hi is multiplied.
, yi ) −
c
2
wφ
2
Exploiting the log-concavity of multinomial logit
(u∗
i , yi ) log
exp(u∗
iyi
)
m[i]
j =1 exp(u∗
ij )

References
GPUA: Experimental Settings
Evaluates accuracy & log-likelihood for real choice data.
Dataset #1: PC (n=1, 088, dX =2)
Dataset #2: SP (n=972, dX =2)
Subjects are asked of choosing a speaker.
A B C D E
Power [Watt] 50 75 100 125 150
Price [USD] 100 130 160 190 220
Choice Set #subjects
{A, B, C} 45:135:145
{B, C, D} 58:137:111
{C, D, E} 95:155: 91
Dataset #3: SM (n=10, 719, dX =23)
SwissMetro dataset (Antonini et al., 2007)
Subjects are asked of choosing one transportation, either
from {train, car, SwissMetro} or {train, SwissMetro}.
Attribute of option: cost, travel time, headway, seat
type, and type of transportation.

References
GPUA: Cross-Validation Performances
High predictability in addition to the interpretable mechanism.
For SP, successfully detected combination of compromise
eﬀect & prioritization of power.
1st best for PC & SP.
2nd best for higher-dimensional SM: slightly worse than
highly expressive nonparametric version of mixed
multinomial logit (McFadden and Train, 2000).
-1.1
-1
-0.9
-0.8
AverageLog-Likelihood
Dataset
PC SP SM
LinLogit
NpLogit
LinMix
NpMix
GPUA
0.3
0.4
0.5
0.6
0.7
ClassificationAccuracy
Dataset
PC SP SM
LinLogit
NpLogit
LinMix
NpMix
GPUA
2
3
4
100 150 200
Evaluation
Price [USD]
EDCBA
Obj. Eval.
{A,B,C}
{B,C,D}
{C,D,E}

Linking ML with Game Theory (GT)
via Shrinkage Principle
Optimization
without shrinkage
Optimization
with shrinkage
ML GT
Maximum-Likelihood estimation
Bayesian estimation Transitional State
or Quantal Response Equilibrium
Nash Equilibrium
Optimal for training data,
but less generalization
capability to test data
Optimal for given game
but less predictable to real-
world decisions
Shrinkage towards uniform
probabilities causes suboptimality
for the given game, but more
predictable to real-world decisions
Shrinkage towards prior causes
suboptimality for training data,
but more generalization capability
to test data

References
Quantitative Handling of Irrationality
Iterative equilibrium computation lightens two natural ways.
Early stopping at step k: Level-k thinking or Cognitive
Hierarchy Theory (Camerer et al., 2004)
Humans cannot predict the inﬁnite future.
Using non-stationary transitional state
Randomization of utility via noise εit: Quantal Response
Equilibrium (McKelvey and Palfrey, 1995)
∀i ∈{1, . . . , n} Y
(t)
i |Y
(t−1)
i = arg max
Y
fi (Y , Y
(t−1)
i ) + εit
Both methods essentially work as regularization of rationality.
Shrinkage into initial values or uniform choice probabilities
Aﬃnity to Bayesian regularization in ML

References
Logit Quantal Response Equilibrium (LQRE)
A special form of QRE associated with RUT.
If εit obeys the standard Gumbel distribution and
Y
(t)
i |Y
(t−1)
i = arg max
Y ∈S
fi (Y , Y
(t−1)
i ) + εit/βi ,
then choice probability becomes closed-form as
P(Y
(t)
i = y|Y
(t−1)
i ) =
exp βi fi (y, Y
(t−1)
i )
y ∈S exp βi fi (y , Y
(t−1)
i )
.
βi is called the degree of irrationality of player i.
βi →0: uniform choice probability (na¨ıve)
βi →∞: Nash equilibrium (deterministic & rational)

Early Stopping and Regularization
ML as a Dynamical System
to find the optimal parameters
GT as a Dynamical System
to find the equilibrium
Parameter #1
Parameter
#2 Exact Maximum-likelihood
estimate (e.g., OLS)
Exact Bayesian estimate
shrunk towards zero
(e.g., Ridge regression)
0
t=10
t=20
t=30
t=50
An early-stopping
estimate
t=0
t=1
t →∞
t=2
...
mean = 50
mean = 34
mean = 15
mean = 0
Nash
Equilibrium
Level-2
Transitional State

References
Towards Useful Decision Making by using QRE
Economists discuss when utility functions {fi }n
i=1 are known.
QRE is analytically-intractable but can be simulated.
E.g., ad-auction for irrational bidders (Rong et al., 2015)
ML scientists should estimate unknown utility functions!
Extension of statistical marketing research methods
through rich functional approximation techniques in ML

References
Multi-Agent Extension of RUT in Marketing
RUT in marketing research has already been data-oriented.
Estimating utility functions from real data
DCM such as Logit model (McFadden, 1980)
Identical opt. objective to multinomial logistic regression
Conjoint analysis (Green and Srinivasan, 1978)
Special case of DCM by showing only 2 options
Related with learning to rank problem: see (Chapelle
and Harchaoui, 2005)
Adding other-player-dependent terms into existing marketing
research models yields a simulation model to compute QRE.

References
Possible Formalisms & Algorithmic Studies
Multi-agent generalization of DCM or learning to rank
Simulation-based ﬁtting (e.g., Approximate Bayesian
Computation (Tavar´e et al., 1997))
Functional approximations (e.g., Gradient Boosting
Decision Trees (Friedman, 2001), Deep Neural Network)
with partially-observable other players’ decisions

References
A Future Forecast: Rise of Deep Belief Learning
Belief Learning (BL) vs Reinforcement Learning (RL)
BL: explicitly guessing other players’ thinking processes
RL: choosing optimal actions purely from experiences
Other players’ decision functions are implicitly parts of
the environment
While predictive accuracies would be similar, BL provides
more white boxes than RL in terms of thinking processes
AlphaGo is a successful application of Deep RL (e.g., (Mnih
et al., 2013; van Hasselt et al., 2016)).
What will be killer applications of Deep BL?

Other Approaches for Irrationality
● Use quantum theory instead of probability
● Quantum Cognition
– (Burza+, 2009; Mogiliansky+, 2009;
de Barros & Suppes, 2009;
Busemeyer & Burza, 2012)
● Key mechanism: double standard
in quantum theory
– During (unobserved) thinking:
integrated over complex state space
– In (observed) decision:
classical probability by taking the
absolute magnitude of state
https://www.amazon.co.jp/Quantum-Models-Cognition-Decision-Busemeyer/dp/1107419883/

Short Summary
● Introduced recent advances on mathematical
modeling of human's irrationality, for more
accurate forecasts.
● Handled irrationality as transional states in both
Machine Learning and Game Theory.
● Importing mathematical techniques from both
ML and GT communities will serve better social
decision making with more accurate forecasts.

WARNING
The following pages exhibit the author's personal
opinions on how to make a good research
direction and/or identify a good area of business.
Effectiveness of these ideas has not been
scientifically proved. Read them at your own risk.

How PhD changed my life
● Before obtaining PhD
● Job: Research Staff Member
in a large B2B enterprise
● Research Topic: Required to
be sticked with one coherent
research direction
● Seeking for problems that
are solvable via my Machine
Learning (ML) disciplines
● After obtaining PhD
● Job: Engineer in a small
B2C startup
● Research Topic: Freedom to
target more ambitious topics
in broader area
● Integrating ML disciplines
with multi-agent perspective
obtained during schooling

Hope and Actuality in PhD Course
● What I was intending
● Exploit ML for
automatically
designing agents.
● Or learn the essence
in manually designing
agents, through
seminar discussions.
● What actually occurred
● Still difficult to know how
to design agents!
– Why this paper's agent
model is designed like this?
● Effective viewpoints on the
design of agents came
after finishing PhD.

Interplay between Research and Job
● Paid Job requires real-world decision making.
● Skin in the game: you cannot use models or
approaches that you do not rely on.
● In order to be confident on your approach,
make focus & apply Occam's razor strongly.
● Avoid using models #1 & #2 & #3 ... Combintion
makes difficult of root-cause finding in failure.
● Define your unique optimization problem, which is
directly solvable by one essential approach.
– Also one-principle-based paper is easily publishable.

Interplay between Research and Job
● A case in job: how to create network externality?
● The key factor in successful platform business
(e.g., Operating Systems, Social Network Services)
● You must have a good mechanism to incentivize
users to use your platform.
● Do the existing mechanisms really incentivize users?
● Are they quantitative to enable real operations?
● Freeing from unrealistic assumptions and practicality
requirement are natual sources of research ideas.

Some Tactics under Competitions
● Development of the truly universal approach =
Red Ocean fought by the World's Top Talents
● Identify the minimum requirement. You create
an approach at least universal in your area.
● Make an approach that competitors dislike to use.
● Such approach often causes disruptive innovation.
● Do not confuse simplicity with naïvety

Necessity of Ample Surveys
● Avoid reinventing wheels. Most industrial
problems have already been partially solved.
● Respect & steal other players' ideas by reading.
● Remember that some prior work is written over-
confidently; prior authors do not know conditions
that spoil their approaches in your new problem.
● Key for success: good strategy to search for
relevant papers and books

Encouraging Bottom-Up Learning
● Check the neighboring disciplines from yours; be
in Optimum Stimulation Level (Berlyne, 1960)
● Your brain is strongly stimulated by insights in slightly distant
areas from your expertise.
● Deep understanding on the very slight difference between two
areas often clarifies the white space in your area.
Machine
Learnng
StatisticsBiostatistics
Econometrics
Psychometrics
Cognitive
Science
Neuroscience
Behavioral
Economics
Behavioral
Game Theory

Uncertainty is Your Friend
● Most people hate uncertainty, but you must love it.
● Further one tactic: beat the irrationality of your competitors!
● The more uncertain parts your research or business contains,
the more competitors will be fooled by too much complexity.
● You: solve the entire problem by one critial solution.
● Competitors: solve each of the sub-problems by its specific
method, and trapped by poor sub-optima.
● Optimism in face of uncertainty

Uncertainty is Your Friend
● Care the difference between risks and uncertainty.
● Risks: volatility calibrated from existing data
● Uncertainty: cannot be quantified from data
● Donald Rumsfeld's unknown unknowns.
● You do not have take high risks. But you should take
high uncertainties.
● In big-data era, competitors rush into the areas with
ample datasets, and become professed with risks.
● By contrast, the human's nature of hating uncertainty
would remain, and it will be a source of your success.

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