Computation,
The Missing Ingredient in
Classical Economics
Edward Tsang
Centre for Computational Finance and Economic Agents
(CCFEA)
University of Essex
Classical Economics
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To model economic relations (often
mathematically)
Start with assumptions
Results follow
Robust…
… as long as the assumptions hold…
Assumptions in Classical Economics
• Computation is taken for granted!
• The Perfect Rationality Assumption
– Everyone can find the optimal solution
• The Homogeneity Assumption
– Everyone can find solutions as good as
others (quality)
– Everyone takes more or less the same
amount of time to find solutions (speed)
• If the homogeneity assumption holds…
– much of computer science is not worth studying
– much of computational intelligence is irrelevant
“Neither can live
while the other survives”
Quote from J K Rowling, “Harry Potter: The Order of Phoenix“, 2003
What is rationality?
What happens when
computation is involved?
Which Option Will You Take?
Which Option Will You Take?
£100 now
or
£10 per month for 12 months
…
What Is Your Move?
• What is the optimal
move?
• Rules are clearly defined
• No hidden information
• Shouldn’t a rational
player pick the optimal
move?
• Problem: combinatorial
explosion!
– Too much to compute!
Computational Intelligence in
Game Theory
Bargaining in Game Theory
Player 1
Player 2
1.2
1
0.8
0.6
r= 0.6
r= 0.6
r= 0.2
0.4
0.2
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Classical approach to Bargaining
• Assume Perfect Rationality
• Player 1 asks:
– What would he offer should he reject my offer?
• Solve this subgame recursively…
• Work out the subgames to infinity, then Player
1 knows what to offer
• Problems:
– Slight alterations to problem  Laborious study
– Solutions absent for slightly complex problems
!! Question: Is this a realistic solution?
Evolutionary Computation in Bargaining
• Our approach: use co-evolution to approximate
subgame equilibrium
Population 1
Modelling player 1
Play against
each other
through
Co-evolution
Population 1
Modelling player 1
• Advantages:
– Capable of handling complex models
– Easy to modify
• Assumption: replace Perfect Rationality by
Reinforcement Learning
Modelling, Simulation and
Machine Learning
Agent-based Computational Economics
1. Model agents & market
Agent 1
Automate the cycle
Through Machine Learning
3. Observe results
Agent 2
Market (e.g. credit card)
Agent n
2. Simulate interactions
4. Modify models in attempt to
achieve desirable behaviour
Computational Intelligence in
Portfolio Optimization
Classical Portfolio Optimization
• Investment basics:
– Maximize return, minimize risk
• Principle: Diversification reduces risk without
compromising return
• Given: a set of assets (S1, S2, …, Sn)
• Task: decide investments, e.g. (7%, 8%, …, 2%)
• Assumptions in Markowitz model:
– No constraint on how much to buy which asset
Efficient Frontier
Fix risk
Max return?
The frontier is
never smooth in
reality!
Multi-objective
optimization
Approximation in Modeling or Solution?
Remote
approximation
How to
pick the
optimal
portfolio?
Closer
approximation
Markowitz’s
simplified
model…
Build
realistic
models…
Modeling:
Financial Expertise required
… which enables
optimal solution
… for which one
can only find
approximations
+
Finding solutions:
Computation Expertise required
So far…
• Bargaining:
– reinforcement learning is a more realistic
assumption than perfect rationality
• Modeling:
– Machine learning could build better models faster
• Portfolio optimization:
– Model  more realistic, optimization  harder
– 2-objectives problem
Economists must face the reality…
Computation Decision Is Complex
Finding the optimal solution
demands a computational cost C
Maximize profit P − C
Sometimes P is timedependent!
Increasing C improves P
(E.g. by employing CI experts)
Hence…
The computational
decision is non-trivial!
How much C improves P by how
much? Unclear when one starts!
Conclusions
• Classical economics took computation for
granted
• The reality is:
– Finding optimal solution is often impossible
– Some can find better solutions than others
– Some can find better solutions faster than others
• Computational Intelligence has major roles to
play in economics and finance!