Method for optimal search on a technology landscape

• US 20010041997A1
• Filed: 02/28/2001
• Published: 11/15/2001
• Est. Priority Date: 08/31/1998
• Status: Abandoned Application

• Current Assignee: NuTech Solutions, Inc. (International Business Machines Corporation)
• Original Assignee: NuTech Solutions, Inc. (International Business Machines Corporation)
• Inventors: Kauffman, Stuart A., MacReady, William G., Lobo, Jose
• Application Number:
  US09/794,017
• Publication Number:
  US 20010041997A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

Technological change at the firm-level has commonly been modeled as random sampling from a fixed distribution of possibilities. Such models, however, typically ignore empirically important aspects of the firm'"'"'s search process, notably the observation that the present state of the firm guides future innovation. In this paper we explicitly treat this aspect of the firm'"'"'s search for technological improvements by introducing a “technology landscape” into an otherwise standard dynamic programming setting where the optimal strategy is to assign a reservation price to each possible technology. Search is modeled as movement, constrained by the cost of innovation, over the technology landscape. Simulations are presented on a stylized technology landscape while analytic results are derived using landscapes that are similar to Markov random fields. We find that early in the search for technological improvements, if the initial position is poor or average, it is optimal to search far away on the technology landscape; but as the firm succeeds in finding technological improvements it is optimal to confine search to a local region of the landscape. We obtain the result that there are diminishing returns to search without having to make the assumption that the firm'"'"'s repeated draws from the search space are independent and identically distributed.

Claims

1. A method for improving a current production recipe, ω

_l,, comprising the steps of;

defining a space of a plurality of production recipes, Ω

;

defining a distance, d, between two of said plurality of production recipes ω

_l, ω

_J ε

Ω

;

defining an efficiency for at least one of said plurality of production recipes;

determining an optimal sampling distance, d*, from the current production recipe ω

_l,; and

searching at said optimal sampling distance d* from the current production recipe ω

_l, for at least one new production recipe ω

_J, wherein the efficiency of said new production recipe ω

_J, is greater than the efficiency of the current production recipe ω

_l,.

2. A method for improving a current production recipe, ω

_l,, as in

claim 1

wherein each of said plurality of production recipes comprises N operations wherein N is a natural number.

3. A method for improving a current production recipe, ω

_l,, as in

claim 2

wherein said distance d between said two of said plurality of production recipes ω

_l, ω

_J ε

Ω

is defined as the minimum number of said N operations that must be changed to convert said production recipe ω

_l, to said production recipe ω

_J,

4. A method for improving a current production recipe, ω

_l,, as in

claim 1

wherein said defining an optimal sampling distance step comprises the steps of;

defining an efficiencies difference function as;

D_d(z)=E(θ

|d)−

z,

wherein;

E(θ

|d) is the expected value of said efficiency at distance d from the current production recipe ω

_l,; and

z is the efficiency of the current production recipe ω

_l,;

defining a reservation price at distance d as the zero crossing value, z_c(d) of said efficiencies difference function; and

defining said optimal sampling distance d* as the distance from the current production recipe having the highest value of said reservation price.

5. A method for improving a current production recipe, ω

_l,, as in

claim 4

wherein said efficiency for at least one of said plurality of production recipes has a Gaussian distribution.

6. Computer executable software code stored on a computer readable medium, the code for improving a current production recipe, ω

_l,, the code comprising;

code to define a space of a plurality of production recipes, Ω

;

code to define a distance, d, between two of said plurality of production recipes Ω

_l, ω

_J ε

Ω

;

code to define an efficiency for at least one of said plurality of production recipes;

code to determine an optimal sampling distance, d*, from the current production recipe ω

_l,; and

code to search at said optimal sampling distance d* from the current production recipe ω

_l, for at least one new production recipe ω

_J, wherein the efficiency of said new production recipe ω

_J, is greater than the efficiency of the current production recipe ω

_l,.

7. A programmed computer system for improving a current production recipe, ω

_l,, comprising at least one memory having at least one region storing computer executable program code and at least one processor for executing the program code stored in said memory, wherein the program code includes;

code to define a space of a plurality of production recipes, Ω

;

code to define a distance, d, between two of said plurality of production recipes ω

_l, ω

_J ε

Ω

;

code to define an efficiency for at least one of said plurality of production recipes;

code to determine an optimal sampling distance, d*, from the current production recipe ω

_l,; and

code to search at said optimal sampling distance d* from the current production recipe ω

_l, for at least one new production recipe ω

_J, wherein the efficiency of said new production recipe ω

_J, is greater than the efficiency of the current production recipe ω

_l,.