Complexity systems management method

• US 20110173146A1
• Filed: 06/12/2007
• Published: 07/14/2011
• Est. Priority Date: 06/12/2006
• Status: Active Grant

• Current Assignee: Projectioneering LLC
• Original Assignee: Projectioneering LLC
• Inventors: Hnatio, John Harris
• Granted Patent: US 8,103,601 B2
• Time in Patent Office: Days
• US Class Current: 706/14
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

The Complexity Systems Management (CSM) Method is a scientifically derived business process method for managing complex events and situations. The CSM Method™ is based on new scientific evidence that explains the behaviors of complex adaptive systems. This same scientific evidence gives rise to a new method of science, known as a priori optionality. A priori optionality is based on six scientifically derived tenets that are systematically applied using the CSM Method™ to more accurately characterize the behaviors of complex adaptive systems and manage complex events and situations. Applications of the CSM Method are integrally tied to specialized knowledgebases and a plurality of automated software applications.

Claims

1. A method of assessing and managing behavior of a complex adaptive system, comprising the steps of:

inputting a first plurality of data defining parameters of said complex adaptive system;

defining a plurality of fundamental events which determine behavior of said complex adaptive system;

modifying at each of a plurality of times at least ones of said first plurality of data to define a plurality of initial conditions;

testing each of said first plurality of data to determine a first subset of said first plurality of data which are most relevant to said plurality of fundamental events for each of said plurality of initial conditions in order to develop a plurality of scenarios of behavior of said complex adaptive system;

measuring an effect of each one of said plurality of initial conditions of each respective one of said developed plurality of scenarios on said first subset of data to provide status information which is capable of being tested to indicate likelihood of an event occurring in said complex adaptive system.

2. The method of claim 1 further including the steps of;

testing each of said scenarios to determine for each scenario precise events which must occur to cause said complex adaptive system to exhibit said scenario; and

determining for each tested scenario critical decision points.

3. The method according to claim 1 including the further step of applying to said status information a first algorithm providing an estimate of an event sequence interruption.

4. The method according to claim 3 wherein values obtained from said applying of said first algorithm provide an event quotient for each of said first subset of data.

5. The method according to claim 3 further including the step of modifying said first plurality of data as a function of a result of said application of said first algorithm.

6. The method according to claim 4 wherein said event quotient further includes a functional relationship based on an algorithm related to occurrence of natural events and an effect of said natural events on said first subset of data.

7. The method of claim 1 wherein said first subset of data are critical nodes of the complex adaptive system.

8. The method of claim 2 further including the steps of:

modifying said first plurality of data to simulate predetermined events occurring in said complex adaptive system;

determining the effects from said simulated events on said critical decision points; and

forming decision fault trees from said determined effects.

9. The method of claim 8 further including forming decision maps and computer models to manage said predetermined events.

10. A method of increasing the likelihood of behavior of a complex adaptive system, comprising the steps:

defining fundamental elements which control the functioning of the complex adaptive system;

assigning a plurality of sets of initial values at a respective plurality of times to a plurality of features of the complex adaptive system;

determining which ones of said plurality of features of the complex adaptive system are most directly related to said fundamental elements for each of said plurality of sets of initial conditions in order to develop a plurality of scenarios of behavior of said complex adaptive system;

measuring an effect of each one of said plurality of sets of initial conditions of each respective one of said developed plurality of scenarios on said ones of said plurality of features most directly related to said fundamental elements to generate sets of data functionally related to the likelihood of a particular occurrence in said complex adaptive system.

11. The method of claim 10 further including the steps of;

testing each of said scenarios to determine for each scenario precise events which must occur to cause said complex adaptive system to exhibit said scenario; and

determining for each tested scenario critical decision points.

12. The method according to claim 10 including the further step of applying to said set of data a first algorithm providing an estimate of an event sequence interruption.

13. The method according to claim 12 wherein values obtained from said applying of said first algorithm provide an event quotient for each of said ones of said plurality of features most directly related to said fundamental elements.

14. The method according to claim 11 further including the step of modifying said plurality of features as a function of a result of said application of said first algorithm.

15. The method according to claim 13 wherein said event quotient further includes a functional relationship based on an algorithm related to occurrence of natural events and an effect of said natural events on said ones of said plurality of features most directly related to said fundamental elements.

16. The method of claim 11 further including the steps of:

modifying said plurality of features to simulate predetermined events occurring in said complex adaptive system;

determining the effects from said simulated events on said critical decision points;

and forming decision fault trees from said determined effects.

17. The method of claim 16 further including forming decision maps and computer models to manage said predetermined events.

18. A computer program product for use with a digital computer for assessing and managing behavior of a complex adaptive system, said computer program product including a computer usable medium having a plurality of computer readable program code means embodied in said medium, comprising;

a first computer readable program code means containing a first plurality of data defining parameters of said complex adaptive system and a plurality of defined relationships which control the functions of the complex adaptive system;

a second computer readable program code means causing a modification at each of a plurality of times at least ones of said first plurality of data to define a plurality of initial conditions;

a third computer readable program code means for testing each of said plurality of data to determine a first subset of said first plurality of data which are most relevant to said plurality of defined relationships for each of said plurality of initial conditions in order to develop a plurality of scenarios of behavior of said complex adaptive system;

a fourth computer readable program code means for determining an effect of each one of said plurality of initial conditions of each respective one of said developed plurality of scenarios on said first subset of data to provide status information which is capable of being tested to indicate likelihood of an event occurring in said complex adaptive system.

19. The computer program product according to claim 18 including a fifth computer readable code means for testing each of said scenarios to determine for each scenario precise events which must occur to cause said complex adaptive system to exhibit said scenario;

and determining for each tested scenario critical decision points.

20. The computer program product according to claim 19 including a sixth computer readable code means for applying to said status information a first algorithm providing an estimate of an event sequence interruption.