Self-Service Decision Intelligence

Transcription

1 Self-Service Decision Intelligence Open Decision Computation: framework for implementing SSDI-based architecture for policy-based enterprise decision management [EARLY DRAFT FOR EXCHANGE] version: presentation is based on visual influence diagrams constructed in Analytica decision software environment, Lumina Decision Systems (both free and paid editions available) Coherence Works LLC TANGENT Management

2 Keywords policy-based enterprise decision management; dynamic decisionmaking; real-time task handling; intelligence amplification; augmented cognition; extended mind; man-computer symbiosis; joint cognitive systems; distributed cognition; embodied cognition; situated cognition; human-computer interaction; transduction; consensus decision-making; mechanization; equivocality; organizational information theory

3 SSDI goals Self-Service Decision Intelligence (SSDI) services should pursue two goals: ecological and ethical operationalization of human (expert s/decision-maker s/analyst s) judgement, and enforcing coagency of the human and machine (joint cognitive system), wherein artifacts are embodied (versus hermeneutically wired) to the degree proven as most economical for the given task decision problem and situation at large.

4 SSDI specifications To achieve these ends any SSDI service/application must meet the following specifications as recommended and advocated by us. Any SSDI dashboard must be process-aware; Any SSDI dashboard must be subroutine-oriented; Any SSDI workbench must be context-situated, i.e. embedded in Bipartite Interface Task Network Architecture (BINTANA) of an enterprise decision system (bintana means casement window in Spanish/Filipino, which bears a metaphor of window of attention, since this architecture is designed to provide inter alia an attention control mechanism (implemented with semantic cueing and saliency) which is a key prerequisite and controller of cognition and driver of consciousness-creation in ambient intelligence settings); Any SSDI workbench must be semantic-model-ready.

5 Role/place of policy-based enterprise decision management Policy-based enterprise decision management architecture based on SSDI framework is intended for tactical business decision-making and planning. It is to support decisionmaking on recurring but still discrete decision tasks/problems. It lies above business rule management (operational level) and below strategic business choices, which is usually highly discrete and unique (non-recurring). Policy-based enterprise decision management also aims at enabling the decision-maker take advantage of information for improving decision quality.

6 Cognitive Contract & Dualized Prospect (CCDP) Theory Theorem of two behavioral bodies: If two behavioral bodies come into interaction with each other in the condition when they have orthogonal constraints to each other, and carry out such interaction joining their drives, after some time their constraints get relaxed and such relaxing takes place in a prolonged manner (timings of the two may or may not match). Dualized prospect theory (DPT): Economic agents take decisions based on the promises that the available choices carry (as the former ascribed to the latter by the agent) for relaxing their constraints (including and firstly changes in the parameters of the drives/drivers themselves) vis-a-vis their drives/drivers. Cognitive contract model (CCM): An agent takes a binary decision to act to initiate a contract with another agent based on his estimation of the other agent s constraint(s) orthogonality to his own constraint(s), as a means of establishing the level of promises along the lines of DPT, while the quantities involved (e.g. the level of match of core drives/drivers of the two), and as assessed/measured by the agent, influence the intensity of his urge only, but not his binary decision (which is a logistic function of the mentioned estimate of his). The agents thus exhibit at the outset an essentially pre-control attitude because they correctly realize that they do not have control over the other agent s constraints in the beginning (which they treat as starting conditions) and for a certain duration on. In other words, this model has an assumption that agents interact in arm s length settings. The theory is a structuralistic development of Cumulative Prospect Theory applied to the contract domain. However, unlike in Prospect Theory/Generalized Expected Utility Model, in CCM the agent s utility and choice is viewed not as that partly arising from and largely shaped by cognitive biases in the first place but as a more rational one based on an objective process and emerging from agent s dynamic Bayesian belief updating-based evaluation of the target constraint(s) based on priors becoming known/available to him by chance and/or by his deliberate information search action(s).

7 Idea of ODC 1) The basic notion is task decision. Task decisions are conceived of in the context of artifactcentric business process view: 2) The second basic notion is structuring task decisions as consisting of drives and constraints as primary aspects. Individual task decision drives make up the drivers of the business (but herein we stick to the term task drivers which is not perfectly correct though, the correct are task drives similar to business drivers ) Individual task decision constraints are the constraints of the business in the fashion of Theory of Constraints.

8 Example 1: Supply Chain Planning raw milk collection supply network from farmers for dairy processing Blue: priors; Green: policies with levers; Pink: objectives/drivers; Sky blue: functional metrics; Orange: throughput accounting KPIs; Red: underlying model engine, including the constraints

9 Example 2: Demand Chain Planning product complementarity- and cannibalization-driven product line rationalization for a retail chain Blue: priors; Green: policies with levers; Pink: objectives/drivers; Sky blue: functional metrics; Orange: throughput accounting KPIs; Red: underlying model engine, including the constraints; Yellow: policy option cost vectors (for demand chain optimization only)

10 Example 3: Value Chain Planning preserved fruit and vegetable processing, production and marketing Here the core (wrapper) model is a decision analysis model for optimizing the value chain operations; next slide shows outputs of the modelling/analysis

11 Example 3: Value Chain Planning (cont.) preserved fruit and vegetable processing, production and marketing Risk analysis of production output depending on purchasing of raw materials Importance analysis of output from inputs (raw materials)

12 Structure of task decision expanded

13 Bipartite interface task decision network as the ODC schema with task decision structures collapsed as to all aspects except drivers and constraints

14 Enterprise decision intelligence system architecture specifications Process-awareness is reflected in accounting for the end-user's constraints in daily operations and task-handling Subroutine-orientedness is reflected in use of computational/process modules external for the given task Context-situatedness (embeddedness) is reflected in Process Impact Control module Operationalization of human judgement is allowed by Choice Strength Parsimony module Human-machine co-agency and semantic-readiness is ensured by co-location of expert and machine semantics

15 Enterprise decision intelligence architecture layers core layers: wrapper model: this is a decision analysis model engine model: this is either decision analysis or DES/ABM/SD simulation or data-mining model (see next slide for details) and auxiliary layers: machine learning algorithm for parameter learning semantic model acquisition machine

16 Taxonomy of modelling methods Practice System / model Model -ling Principal approach Logic Method Use Outcome/ purpose Control / planning Operation mode Business goal Structured (for the wrapper model at least), connected Value Chain Optimization Whitebox Closedloop: feedforward Decision Analysis Deductive Analysis with a priori information Problem structuring (management system purposefulness) Online Discrete Choices (decisionmaking) Development (explicability) Demand Chain Optimization Unstructured, open Blackbox Closedloop: feedback Datamining / machine learning Inductive Synthesis with a posteriori sampled data Dynamics analysis (environment / market selforganization) Ad Hoc Campaigns Runtime Growth (effectiveness) Supply Chain Optimization Semistructured closed Greybox Openloop Simulation Modelling Deductive for local and inductive for global Analysis/a priori info for micropicture and synthesis/a posteriori experimental data for macro Continuous Planning Complexity analysis (functional and process interdependencies) Realtime Costcutting (efficiency)

17 Model/parameter estimation and adaptation mechanisms There are three mechanisms of learning that can be designed and deployed into the architecture: White-box parameter sourcing/tuning: this is done through updating the parameter values directly from the underlying engine (be it decision analysis, simulation modelling or data-mining-mining model). This is executed on regular or ad hoc basis by running the underlying engine. Black-box parameter sourcing/tuning: this is done automatically with a machine learning algorithm. The role of this mechanism is to have the ODC network learn and adapt with experience. Finite state automata: based on the previous two mechanisms the ODC network acts also as a finite state machine meaning that over time it acquires the semantic model of the business, which it manages adaptively, including with regard to periods of stationarity. (This may possibly allow to run Markov Chain Monte Carlo for Bayesian analysis and else to supply also predictive functions.)

18 Black-box + Finite State Machine + Semantic Model Acquisition expanded (early draft) Concepts Reinforcement Learning (RL) Relational MDPs (RMDP) (Martijn van Otterlo, 2009) First-order-represented MDPs (FORM) (Martijn van Otterlo, 2009) CARCASS (Martijn van Otterlo, 2003, 2004) PIAGeT principles (Martijn van Otterlo, 2009) Intensional Dynamic Programming (IDP) (Martijn van Otterlo, 2009) Generalized Policy Iterations (GPI) (Sutton and Barto, 1998) other concepts, appoaches, methods, techniques and algorithms in adaptive sequential reasoning, decision-making and learning

19 Task decision architecture construction principles Task decision encapsulation Function punctualization Process punctualization Choice between embodied (amplifier) versus hermeneutic (interpreter) relations Choice between tool (DSS) versus prosthesis (expert system) Open ontological model for broader connectivity SOA-like architecture

20 Task decision interface rules Loose coupling Description: [to be added] Open Decision principle Description: [to be added] Ramification control/cap Description: [to be added]

21 ODC DB schema [a screenshot of the valid EER diagram of reference ODC DB must be below, instead of below example taken from MySQL Workbench CE]

22 ODC modes 1. Integrated (online, semi-automated): [to be added] 2. Real-time collaborative: [to be added] 3. Tactical (offline): [to be added] 4. Project/campaign (run-time): [to be added]

23 Implementation of ODC: activities Data modelling Data management process modelling Software and integration choices Task decision modelling Decision analysis Simulation modelling Data-mining/machine learning Decision management process modelling Business process and architecture simulation and analysis Continuous improvement, fine-tuning and optimization

24 Implementation of ODC: computation Choice of software environment SMILE (Structural Modeling, Inference, and Learning Engine), Decision Systems Laboratory, University of Pittsburgh: Analytica decision software and engine, Lumina Decision Systems: World Modeller, Quantellia: DecisionFirst Modeller, Decision Management Solutions: SEAS: Structured Evidential Argumentation System, SRI International, AI Builder, TinMan Systems, other Integration IT infrastructure Plugging of external modules/models Discrete event simulation Agent-based modelling System dynamics Data-mining platform Machine learning algorithms

25 Implementation of ODC: alignment [to be elaborated]

26 [to be elaborated] Cases for SSDI/ODC

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28 Thank you Hayk Antonyan Coherence Works LLC [a] 30 Sebastia str., #19, P.O. Box 0004, Yerevan, Armenia [e] [t] +374 (0) [f] +374 (0)