Market-driven innovation management,

from the inside in

Georgios G. Tziralis & Ilias P. Tatsiopoulos, NTUAAPMS 2008, Sep 15-17, Helsinki

Enterprises and organizations seem like market-agnostic, semi-permeable bubbles, in a market-

powered universe.

the problem

• Innovation management and idea selection

• in general:

- n potential evaluators of ideas

- m ideas,

- evaluated by i variables each

• optimal solution: the evaluation of an omniscient agent

background

• need for efficient information aggregation

• tools in use so far vary

- nominal groups, brainstorming, discussion boards and voting systems

- poor for information evaluation (static, low motive, etc)

• other options?

markets

• bring people together

• trigger conversations

• sum up information

• transmits it through prices

- idea selection is a distributed computation and markets seems like a fitting tool...

the approach

• each trader begins with her own information

• by observing market prices,

- she learns or infers what information other traders are likely to have

- she can then update her own info and beliefs

• over time, all information is revealed through trading, market price and traders beliefs converge

questions

1. Does a prediction market converge to a consensus equilibrium?

2. If yes, how fast is the convergence process?

3. What is the best possible equilibrium of a prediction market? and

4. Is a prediction market guaranteed to converge to the best possible equilibrium?

the model

• model design eternally balances on the trade-off of realistic detail and meaningful simplicity

- this one is biased towards simplicity

• information structure

• market mechanism

• agent strategies

information structure

• (boolean) state space of the world S = {0,1}m

• traders’ information space X = {0,1}n

• initially, each trader is privy to one bit of information xi (input bit)

• conditional distribution of information

- Q(x|s): {0,1}m x {0,1}n →[0,1]

- common knowledge to all traders

ehm?

• x1=1 (x1=0): product (not) viable for production

• assume one-dimensional state space (s=s1)

• P(x1=1 | s=1) = 0.9

• P(x1=0|s=1) = 0.1

- aggregation uncertainty!

• agent i starts with xi, but what about input bits of other traders?

market mechanism

• target: predict the value of f(s)

- f(s) : {0,1}m x {0,1}n →[0,1]

- s: state of the world

- f: boolean, common knowledge to all traders

market mechanism

• pari-mutuel market (Pennock 2004)

• two securities k, l, one for each binary outcome

• price(k), price(l) ∈ [0,1] (continuous variable)

• initially price(k) = price(l) = 0.5

• if finally f(s) = 1, all money are re-distributed to the owners of security k, and vice versa

market mechanism

• a multi-period Shapley-Shubik market game

• market proceed in synchronous rounds

• on each round, each agents buys one share of a security, either k or l

• after each round, new prices are announced

• price(k) = quantity(k) / (quantity(k)^2 + quantity(l)^2)

• process continues till equilibrium

agent strategies

• risk neutral

• myopic

• bid truthfully

• act rationally, each time submitting their valuation of securities’ expected payoff, updated via Bayes’ rule

• these are common knowledge

deductions

1. price convergence

2. convergence speed

3. best possible prediction

4. convergence to the best prediction or not

price convergence

• Without the arrival of new information, the market converges to equilibrium in finite steps

• At equilibrium, all traders have the same expectation about the value of f(s), which equals the equilibrium market price

convergence speed

• The market converges to equilibrium after at most n rounds of trading

- n is the number of traders

best possible prediction

• The best possible prediction that the market can make is the forecast at direct communication equilibrium

- namely the equilibrium that is reached immediately when all market traders directly reveal their private information to each other, i.e. E(f(s)|x)

convergence to best prediction

• An information market is not guaranteed to converge to direct communication equilibrium

discussion

• introduced a novel market approach to idea selection for innovation management

• formalized a simplified model of such a market process, with promising results

- convergence to equilibrium in relatively few steps, a state fully descriptive of available to traders information

• paved the way for further research (more advance model, simulations, experiments)