The corporation as a complex adaptive system (CAS)
Erie resemblance...
Boeing 777 freighter assembly line: http://www.youtube.com/watch?v=AiKIC8ztqhY&feature=related
Ants create a lifeboat in the Amazon jungle - BBC wildlife: http://www.youtube.com/watch?v=A042J0IDQK4
How did corporations come about?
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What exactly is the invisible hand?
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How do free markets work?
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Corporations spring to life spontaneously from among competing agents, but
how?
Are “markets forces” akin to evolution and natural selection?
Evolution and natural selection
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All forms of life on Earth originate from a primitive common ancestor
(hypothesis)
All life forms are self-organizing and evolve from simple to complex
spontaneously without an aim or goal (fact)
The environment favors the survival of species that are most adapted to
certain conditions (fact)
Since environmental conditions change continuously, no species are ever fit
to survive (fact)
Evolution and natural selection are deterministic, although they might appear
to us as random (fact)
The most common misconceptions about evolution and
natural selection
“The survival of the fitness” - the wrong metaphor, too often applied to
business to justify opportunism
“Evolution and natural selection rely on chance” - evolution is not random
“Evolution is just a theory” - evolution and natural selection are made up of facts
Social life = vast networks of individuals
Isolated individuals
Very sparse networks
Sparse networks
Dense and very dense networks
How do networks behave?
Basic business question:
Given asset A that has a payoff P, and each individual preference p, how many
individuals will end holding A?
How will behavior change as time goes by?
Let us make no unnecessary assumptions...
Isolated individuals
For each state of the economy from 0 to N:
If P > p, then hold asset A;
Otherwise do nothing
Group of isolated agents
Susan
Rob
Mike
PaulL
PaulG
Bill
Sylvie
Mark
Steve
Lourdes
Hafid
Cranmer
Cranmer
Denise
Yanan
Isolated agents
Aggregate behavior of a group of isolated agents
4.5
4
3.5
3
Total
2.5
2
1.5
1
0.5
Iterations (50)
0
Demand (A) = P-1
The demand for asset A
16
14
12
Number of agents holding asset A
10
8
6
4
2
0
1
2
3
4
5
6
7
P =Payoff
8
9 from
10 asset
11 A
12
13
14
15
16
17
18
19
20
Individuals are social creatures
Size of social group is proportional to relative size of the brain
Individuals compete and/or cooperate
Each individual's social strategy is based on what other individuals are doing
Four mechanisms for achieving cooperation
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Kinship is based on shared DNA. People who share the same DNA have a
natural propensity to help each other to ensure the preservation and
transmission of their genetic material (the most wide-spread type of firm is
the family business).
Social capital is based on trust. Face-to-face interaction plays an essential
role in bonding. Larger groups are less cohesive. In order to work, social
capital requires shared values and culture.
Contracts are based on property rights. To work, it requires the power to
enforce contracts.
The Government is based on coercion. Governments have a monopoly on
physical coercion.
Very sparse network
Susan
Rob
Mike
PaulL
PaulG
Bill
Sylvie
Mark
Steve
Lourdes
Hafid
Cranmer
Camille
Denise
Yanan
Very sparse networks: How do individuals interact? An
example
For each state of the economy from 0 to N:
If P > p, then 1;
Otherwise 0
For Lourdes only:
If there is no change in P; and
If in the previous iteration Sylvie is 1, then 1;
Otherwise 0
For Bill only:
If there is no change in P; and
If in the previous iteration Steve is 1, then 0;
Otherwise 1
For Sylvie only:
If there is no change in P; and
If in the previous iteration Lourdes is 1, then 1;
Otherwise 0
For Susan only:
If there is no change in P; and
If in the previous iteration Yanan is 1, then 1;
Otherwise 0
For Rob only:
If there is no change in P; and
If in the previous iteration Mike is 1, then 1;
Otherwise 0
For Yanan only:
If there is no change in P; and
If in the previous iteration Susan is 1, then 1;
Otherwise 0
For Mike only:
If there is no change in P; and
If in the previous iteration Rob is 1, then 1;
Otherwise 0
For Mark only:
If there is no change in P; and
If in the previous iteration Camille is 1, then 1;
Otherwise 0
For PaulG only:
If there is no change in P; and
If in the previous iteration Steve is 1, then 0;
Otherwise 1
For Camille only:
If there is no change in P; and
If in the previous iteration Mark is 1, then 1;
Otherwise 0
Very sparse networks: How do individuals interact? An
example
Lourdes and Sylvie, Rob and Mike, Susan and Yanan, and Camille and Mark,
always make similar decisions based on the previous iteration; while PaulG and Bill do
the opposite of what Steve did in the previous iteration.
Very sparse network behavior
Agregate behavior of a very sparse network
6
5
Total
4
3
2
1
0
Iterations (370)
Very dense networks
In a very dense network every agent interacts with everybody else
For each state of the economy from 0 to N:
If P > p, then 1;
Otherwise 0
If there is no change in P; and
If in the previous iteration
Agent(i) AND Agent(j) OR Agent(k) NOT Agent (m) OR Agent(n)... etc.... is 1, then 1
Otherwise 0
Aggregate behavior of a very dense network
Initial condition P = 10
16
14
12
Total
10
8
6
4
2
0
Iterations (350)
Very dense networks
The configuration of the network (the allocation of asset A) changes through time
without changes in individual preferences or changes in the payoff P.
The network is oscillating on its own without help or input from the “outside”
environment.
With each iteration the network visits yet another configuration. Total possible
configuration states = 215 = 32,768
In the worst case scenario the network will cycle through all possible 32,768 states
Very dense networks – predicting behavior
A small change in the payoff P (say 11 instead of 10) results in a vastly different
pattern of behavior – the butterfly effect
Predicting behavior:
Must know individual preferences
Must know each interaction rule
Must know the initial payoff P
Must have lots of computational power and speed
There are no shortcuts, one must compute every possible state.....
Sparse networks
In sparse networks, each individual interacts with about two or three other
individuals. Example:
For each state of the economy from 0 to N:
If P > p, then 1;
Otherwise 0
If there is no change in P; and
If in the previous iteration (right neighbor AND yourself AND left neighbor) are all
1, then 0
If in the previous iteration (right neighbor AND yourself AND left neighbor) are all
0, then 0
If in the previous iteration (yourself AND left neighbor) are 0; AND (right
neighbor) is 1; then 0
Otherwise, 1
Sparse networks
Aggregate behavior of a sparse random network
Initial condition P = 5
14
12
Total
10
8
6
4
2
0
(150)
Aggregate behaviorIterations
of a sparse
random network
Initial condition P = 4
14
12
Total
10
8
6
4
2
0
Aggregate behavior of a sparse random network.
Iterations (150)
Initial condition
P = 13
14
12
Total
10
8
6
4
2
0
Iterations (150)
Sparse networks: another example
For each state of the economy from 0 to N:
If P > p, then 1;
Otherwise 0
If there is no change in P;and
If in the previous iteration (two neighbors to your right) are both 0 OR both 1,
then 0
otherwise 1;
For Yanan: If in the previous iteration (Mark AND Camille) are 1, then 1
otherwise 0;
For Denise and Camille: If in the previous iteration (two neighbors to your right)
are both 1, then 1
otherwise 0
Aggregate behavior of a sparse random network
Initial condition P = 5
12
10
Total
8
6
4
2
0
Iterations (350)
Aggregate behavior of a sparse random network
Initial condition P = 2
7
6
Total
5
4
3
2
1
0
Iterations (350)
Aggregate behavior of a sparse random network
Initial condition P = 4
9
8
7
Total
6
5
4
3
2
1
0
Iterations (350)
Sparse networks: Example #3
For each state of the economy from 0 to N:
If P > p, then 1;
Otherwise 0
If there is no change in P; and
If in the previous iteration (two neighbors to your right) are both 0 OR both 1,
then 0;
otherwise 1
Only for Steve and Mike: If in the previous iteration (two neighbors to your right)
are both 1, then 1;
otherwise 0
Aggregate behavior of a sparse random network
Initial condition P = 5
16
14
12
Total
10
8
6
4
2
0
Iterations (350)
Aggregate behavior of a sparse random network
Initial condition P = 3
14
12
Total
10
8
6
4
2
0
Aggregate behavior of a sparse random network
Initial condition
P= 7
Iterations (350)
16
14
12
Total
10
8
6
4
2
0
Iterations (350)
Sparse networks: Example #4
For each state of the economy from 0 to N:
If P > p, then 1;
Otherwise 0
If there is no change in P; and
If in the previous iteration (two neighbors to your right) are both 0 OR both 1,
then 0
otherwise 1
Aggregate behavior of a sparse network
Initial condition P = 3
12
10
Total
8
6
4
2
0
Iterations (350)
Aggregate behavior of a sparse random network
Initial condition P = 6
14
12
Total
10
8
6
4
2
0
Iterations (350)
Sparse (critical) networks
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Represent a mix of order and chaos – poised at the edge of chaos
Changes in behavior (pattern) are driven by changes in interaction among
individuals
Networks
[Initial condition (state)] AND [Interaction rule] => [System behavior]
Complexity
Simple patterns (behavior): can be easily described with a shorthand formula
Complex patterns (behavior): cannot be described with a shorthand formula
Complexity of behavior
Behavior complexity as a function or rule complexity
Complexity of the behavior generating rule
Example of sparse (critical ) networks
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Genome (DNA)
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Living organisms
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Bee hives
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Ant colonies
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PCs
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Social networks
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Corporations
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Etc.
Properties of critical (sparse) networks
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Emergent behavior
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Self-organization (auto-catalytic)
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Homeostasis (stability through time)
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Feedback loops
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Nesting (hierarchies of complexity)
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Prediction, learning, and adaptation
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Undecidable and computationally irreducible
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Universality (capacity to emulate or mimic ANY type of behavior)
Critical networks: Undecidable and computationally
irreducible
[Initial condition (state)] AND [Interaction rule] => [System behavior]
(i) Given some initial conditions and interaction rules it is hard to tell whether the
network will reach a given configuration (here asset allocation) – the only way to
find out is to run the system in real time
(ii) Given a certain configuration (here asset allocation) it is hard to deduce the
initial conditions and the rules that generated it.
General implications
All systems generate patterns – but not all patterns are easily recognizable.
At times, complex behavior might appear to us as random (Randomness: lack of
pattern). At times, complex behavior might appear to us as ordered and simple.
We are justified in looking for patterns (complex critical systems tend to be stable) –
our brains are wired to look for patterns (best prediction of the future is the past)
Patterns are not necessarily the result of purposeful action (complex system are
spontaneous and self-organizing)
Side note on randomness and patterns
Randomness (chance) is the lack of pattern.
Complex pattern appear random when we cannot figure out the sequence
Since business is based on interaction rules, hardly anything is truly random. We
have difficulty recognizing patterns because of computational
irreducibility.
Unfortunately, we also confuse patterns with purpose because we are wired to
believe that only purposeful action generates patterns
More implications...
Critical systems are undecidable: one cannot know if a given system will ever
reach a particular configuration (state)
We do not know how to solve optimization problems.
Example:
Maximization of PV(CF) = f[future CF]
future CF = f[Maximization of PV(CF)]
Maximization of PV(CF) = f[Maximization of PV(CF)] => one ends up moving in
circles
CF prediction paradox
Make a CF prediction
Take action based on this prediction
Action changes future CF
Did one already predict the change in CF due to the prediction?
(This is the logic behind market efficiency – there are no overpriced stocks,
because if there were, they would have been snatched up by other investors
already)
Common sense intuition:
PV optimization is hard because it is almost impossible to keep track of
how a large number of tightly inter-connected variables might change
simultaneously
More implications...
Critical systems are computationally irreducible: given a certain configuration
(state) it is hard to know how the system got there....unless one keeps track of
every past change.
Given ROE, ROA, etc.....how much of this performance is due to the CEO? Or
COO? Or financial markets? Or rising oil prices?
In general, performance measuring is problematic
How can you tell a complex network?
General rule of thumb: Look for feedback loops
Critical systems are self-referential (they have feedback loops)
Positive feedback (+reinforcement): market bubbles, crashes, fads,
fashion,nuclear chain reaction, addictions, etc.
Negative feedback (-reinforcement): thermostats, biological regulators, etc.
Network stability relies on the balance between (+) and (–) feedback
A comparison of various types of networks
Network type
Interaction among
agents
Initial conditions
Behavior
None
Movement due only to
external shocks.
Very simple linear behavior.
Classical equilibrium. Frozen in time,
changes are only due to external
(random) shocks. Reducible to simple
mathematical formulas.
Very sparse
networks
(subcritical)
Some agents' decisions
are infl uenced by other
agents' previous period
decisions by means of
very simple rules.
Movement due mainly to
external shocks
Simple linear, or oscillating behavior.
Relatively predictable. Reducible to
simple mathematical formulas.
Sparse networks
(critical)
All agents' decisions
are infl uenced by two
other agents' previous
decisions by means of
very simple rules.
Self-organized and steady
going. Self-referential,
autocatalytic, multiple feedback loops.
Very complex behavior. Although
ordered and structured, behavior is
relatively hard to predict. Capable of
learning, and adaptation. Not
reducible to simple mathematical
formulas. Undecidable. The simplest
model of the system is the system
itself.
The system is poised at the edge of
chaos (between equilibrium and
chaos).
Isolated agents
Very resilient to changes in
initial conditions. A majority
of changes in initial
conditions are without
consequences. Changes in
patterns of behavior are
triggered by changes in
interaction rules.
Very dense
networks
(supercritical)
All agents' decisions
are infl uenced by all
other agents' decisions
by means of very
simple rules.
Very sensitive to initial
conditions. Small changes in
initial conditions trigger
catastrophic changes in
behavior
Some systems are capable of
universal computations, emulating
the behavior of any other known type
of system.
Chaotic and unpredictable, complex
behavior. Strange attractors. Cycle
through all possible states before
repeating. Irreducible.
The corporation defined
The corporation is a self-organized, critical, and sparse network of agents.
Individuals are purposeful and conscious agents, yet the fine behavior
characteristics of the corporation were neither foreseen nor planned in advance.
The perceived aims and goals of the corporation merely represent the way in
which individuals rationalize their purposeful actions. We rationalize the firm as an
attempt to make profits.
The interaction rules among agents are defined by economic contracts and social
norms. The firm is thus a nexus of contracts.
Economic contracts are generally based on property rights and delineate the legal
boundaries of the firm (they keep the network sparse and critical)
The corporation....
A system of financial claims on economic resources
The claims are distributed according to the willingness and ability to cope with
uncertainty
(i) Fixed claims: bonds and other fixed-income securities, etc.
(ii) Residual claims: shareholders
Forms of business organization
Proprietorship and partnership: based on kinship and social capital (trust)
Corporation: based on enforceable contracts and social capital (shared cultural
values)
The corporate veil: the corporation is a separate legal entity from its owners – it
can enter contracts, it can sue, it has freedom of speech, etc.
The corporation: Separation between ownership and control
Shareholders – individuals who are specialized in providing capital and bearing
firm-specific risks
Managers – individuals who are specialized in making decisions
The corporation: Free markets vs. administrative authority
Free markets are spontaneous, self-organizing networks were interaction takes
places based on supply and demand – one uses market prices to decide what to
do (the invisible hand).
Markets sometimes become dense networks behaving chaotically.
Corporations are are spontaneous, self-organizing networks were interaction
takes places based on administrative authority – one is told what to do (the iron
fist).
Corporations emerge from but are inimical to free markets. As corporations
expand and grow, free markets retreat and whither. Corporations draw the
boundary between competition and cooperation.
What are the advantages of administrative authority?
It constraints the network to a sparse (critical) configuration capable of
learning and adaptation
When bargaining and contracting is costly, administrative decisions rationalize
the interaction among individuals.
Example: 100 contracting parties: 1 CEO, 11 directors, 8 lawyers, 50
shareholders, and 30 employees.
Without a central administrative authority (the board of directors), each party
would have to negotiate and contract with each other:
C(100, 2) = 100!/2!(98!) = (1*2*3*....*100)/(1*2*2*1*2*3*....*98) = 4,950 contracts
With a central administrative authority there are only 100 contracts needed.
The corporation: Private vs. public goods
Individuals are willing to cooperate if there is a payoff (profit) – this is how we
rationalize compliance with administrative authority.
Corporations internalize as many benefits and externalize as many costs as possible.
Example: Chemical plants do not voluntarily adopt eco-friendly technologies because
the costs of these technologies are borne solely by the company, yet the benefits are
spread to pretty much everyone.
Corporations are generally providing goods that cannot be shared and are exclusive
(private goods)