Method and apparatus for creating and evaluating strategies
First Claim
1. An iterative method for creating and evaluating strategies, comprising the steps of:
- providing any of;
a team development module for developing a strategy modeling team;
a strategy situation analysis module for framing a decision situation;
a data request and reception module for designing and executing logistics of specifying, acquiring, and loading data required for decision and strategy modeling;
a data transformation and cleansing module for verifying, cleansing, and transforming data;
a decision key and intermediate variable creation module for computing additional variables from data and constructing a data dictionary;
a data exploration module for determining characteristics that are effective decision keys and intermediate variables;
a decision model structuring module for formalizing relationships between decisions, decision keys, intermediate variables, and value of a decision model;
a decision model quantification module for encoding information into a decision model;
a strategy creation module for determining strategies that a client can test; and
a strategy testing module for testing strategies to guide refinement of strategies and refinement of a decision model and to select a best strategy for deployment;
wherein each of said modules has capability to interact with an expert task manager, wherein said expert task manager provides expert knowledge about strategy modeling processes and sub-processes.
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Abstract
A method and apparatus for strategy science methodology involving computer implementation is provided. The invention includes a well-defined set of procedures for carrying out a full range of projects to develop strategies for clients. One embodiment of the invention produces custom consulting projects that are found at one end of the full range of projects. At the other end of the range are, for example, projects developing strategies from syndicated models. The strategies developed are for single decisions or for sequences of multiple decisions. Some parts of the preferred embodiment of the invention are categorized into the following areas: Team Development, Strategy Situation Analysis, Quantifying the Objective Function, Data Request and Reception, Data Transformation and Cleansing, Decision Key and Intermediate Variable Creation, Data Exploration, Decision Model Structuring, Decision Model Quantification, An Exemplary Score Tuner, Strategy Creation, An Exemplary Strategy Optimizer, An Exemplary Uncertainty Estimator, and Strategy Testing. Each of the sub-categories are described and discussed in detail under sections of the same headings. The invention uses judgment in addition to data for developing strategies for clients.
444 Citations
71 Claims
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1. An iterative method for creating and evaluating strategies, comprising the steps of:
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providing any of;
a team development module for developing a strategy modeling team;
a strategy situation analysis module for framing a decision situation;
a data request and reception module for designing and executing logistics of specifying, acquiring, and loading data required for decision and strategy modeling;
a data transformation and cleansing module for verifying, cleansing, and transforming data;
a decision key and intermediate variable creation module for computing additional variables from data and constructing a data dictionary;
a data exploration module for determining characteristics that are effective decision keys and intermediate variables;
a decision model structuring module for formalizing relationships between decisions, decision keys, intermediate variables, and value of a decision model;
a decision model quantification module for encoding information into a decision model;
a strategy creation module for determining strategies that a client can test; and
a strategy testing module for testing strategies to guide refinement of strategies and refinement of a decision model and to select a best strategy for deployment;
wherein each of said modules has capability to interact with an expert task manager, wherein said expert task manager provides expert knowledge about strategy modeling processes and sub-processes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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28. An apparatus for iteratively creating and evaluating strategies in an iterative, comprising:
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means for providing any of;
a team development module for developing a strategy modeling team;
a strategy situation analysis module for framing a decision situation;
a data request and reception module for designing and executing logistics of specifying, acquiring, and loading data required for decision and strategy modeling;
a data transformation and cleansing module for verifying, cleansing, and transforming data;
a decision key and intermediate variable creation module for computing additional variables from data and constructing a data dictionary;
a data exploration module for determining characteristics that are effective decision keys and intermediate variables;
a decision model structuring module for formalizing relationships between decisions, decision keys, intermediate variables, and value of a decision model;
a decision model quantification module for encoding information into a decision model;
a strategy creation module for determining strategies that a client can test; and
a strategy testing module for testing strategies to guide refinement of strategies and refinement of a decision model and to select a best strategy for deployment;
wherein each of said modules has capability to interact with an expert task manager, wherein said expert task manager provides expert knowledge about strategy modeling processes and sub-processes. - View Dependent Claims (29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54)
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43. The apparatus of claim 90, further comprising:
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means for providing any of six dimensions associated with any of six links in a decision quality chain, said six links comprising;
appropriate frame;
creative-feasible alternatives;
meaningful-reliable Information;
clear values and tradeoffs;
logically-correct reasoning; and
commitment to action;
wherein said chain supports an organization'"'"'s value.
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55. An apparatus for automating decision model updating and reporting, comprising:
at least one tuning apparatus, comprising any of;
data awareness capability;
triggering rules;
model history retention;
self-guided model development;
connection to a decision engine; and
means for triggering a parameter tuning run execution and analytic audit trails; and
means for reviewing results, wherein said results are either accepted and an update is deployed, or rejected. - View Dependent Claims (56)
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57. A decisioning client apparatus, comprising:
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a decisioning client application processing system for;
supplying data associated with a customer to a decision engine; and
requesting a decision; and
wherein said decision engine comprises a score generation module;
means for said decision engine, using said score generation module, generating needed transformations of said data and generating at least one score, said at least one score based on at least one score weight of at least one scorecard at a time;
means for said decision engine applying pre-specified decision rules and strategies using said data and said transformed data, and at least one score for generating a vector of recommended decision actions;
means for said decision engine returning requested data, said transformed data, said at least one score, information about said at least one scorecard, and said recommended actions to said decisioning client application processing system;
means for said decisioning client application processing system optionally implementing said recommended actions, and storing results into a data store. - View Dependent Claims (58)
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59. A score tuner method, comprising the steps of:
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providing a score tuning broker module for performing administrative tasks associated with updating of score weights, said score tuning broker module comprising the steps of;
determining which scorecards are candidates for tuning;
checking any operating scorecards are flagged for updates; and
at a pre-specified and parameterized time frequency, determining from a rule database which scorecards are up for score weight re-tuning;
extracting needed data set sub-population based on rules determining what sampling window and stratification a current scorecard needs;
for a scorecard that is a candidate for re-tuning for the current time stamp;
requesting generation of a data set to be used for said tuning; and
determining what score weight engine project is associated with said scorecard;
passing a reference to said data set and a project id to said score weight engine, and requesting metrics of scorecard performance from said score weight engine; and
determining whether updated version is better or not; and
providing a score weight engine module for performing activities related to scorecard results and score weights, said score weight engine module comprising the steps of;
reporting on an existing scorecard'"'"'s development measures;
computing a scorecard'"'"'s performance measures on a new sample;
auditing new predictive data set to ensure that settings are adequate to cover data values encountered in said new data;
creating a new scorecard version of said scorecard being tuned;
converting raw records in said new predictive data set into coarse classed records needed for building weights;
building and scaling score weights of said newly created scorecard given said new predictive data; and
archiving said newly built scorecard and its performance measures. - View Dependent Claims (60)
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61. A score tuner method, comprising the steps of:
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providing rapid weights tuning for modifying score weights of a scorecard; and
/orproviding rapid score alignment for aligning parameters of said scorecard;
wherein said underlying structure of said scorecard'"'"'s data is not different from original implementation definition. - View Dependent Claims (62, 63, 64, 65)
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66. A score tuner apparatus, comprising:
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a database manager component for managing collection of cases used in analysis, and for providing a bridge to multiple possible input data files and/or database management systems;
a data manager component for providing data records to other data analysis components, one case at a time in the event that said data analysis components are processing cases in a sample point loop, for exposing a data dictionary to other components, and for allowing posting variables generated in said data analysis components back to said database manager for future recall;
a modeler component for providing score weight re-optimization and for logging odds to score alignment functionality;
a report collection component for providing viewing, printing, and limited editing of a standard set of model evaluation reports generated by said modeler;
a workflow controller for controlling flow of multiple business components performing a set of actions that are implied by user specifications and eventually fulfilling desired data preparation, analysis, and/or presentation steps; and
an intelligence agent for performing background checks on results from user actions and for providing suggestions if a query against its rule base returns a recommended intelligent action to take. - View Dependent Claims (67)
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68. A system for estimating an uncertainty interval around at least one estimate of at least one expected outcome, comprising:
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an input device operable to allow entering and transferring input data to a processor;
an output device for displaying human readable results of manipulation of said input data;
one or more communications buses between said input device and said processor and said output device and said processor, respectively; and
said processor comprising a memory, wherein said memory stores at least one program for quantifying said uncertainty interval due to variation based on case-level variation, model variation, and portfolio composition, said program performing a sequence of instructions, the sequences of instructions, which, when executed by said processor, cause the processor to perform the steps of;
causing a decision model to encapsulate case-level variation;
implementing non-parametric bootstrapping techniques to capture model variation;
using analysis of historic data on holdout samples to describe case-level error distributions; and
capturing portfolio composition variation as an integral element of said quantifying said uncertainty interval process. - View Dependent Claims (69)
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70. A method for estimating an uncertainty interval around at least one estimate of at least one expected outcome, comprising the steps of:
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providing an input device operable to allow entering and transferring input data to a processor;
providing an output device for displaying human readable results of manipulation of said input data;
providing communications buses between said input device and said processor and said output device and said processor, respectively; and
said processor comprising a memory, wherein said memory stores at least one program for quantifying said uncertainty interval due to variation based on case-level variation, model variation, and portfolio composition, said program performing a sequence of instructions, the sequences of instructions, which, when executed by said processor, cause the processor to perform the steps of;
providing a decision model to encapsulate case-level variation;
implementing non-parametric bootstrapping techniques to capture model variation;
using analysis of historic data on holdout samples to describe case-level error distributions; and
capturing portfolio composition variation as an integral element of said quantifying said uncertainty interval process. - View Dependent Claims (71)
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Specification