Approach
Lexidyne uses the Dynamic Modeling methodology to help our clients understand, communicate, quantify, and improve the performance of a wide range of complex processes. Dynamic Modeling has 5 key concepts:
Dynamic Modeling Key Concepts
An Icon-Based Language
- An Icon-based Language that operationally describes how dynamic processes actually work. The language distinguishes between things that accumulate (stocks) and actions that fill or drain those accumulations (flows).
- Augmenting the picture are auxiliaries, which can serve as model inputs, house simple algebra, or depict dependent relationships between system elements.
- Information conduits (connectors) show how model variables relate to one another.
Feedback Between System Components
- Feedback between system components details the chain of cause-and-effect relationships that drive system behavior.
- Often such feedback relationships include decision rules which result in unintended, sub-optimal outcomes.
Behavioral Data
- Behavioral data, often collected from disparate data sources, which is analyzed and incorporated into the model structure to serve as inputs for various parameter estimates and/or metrics to which model results can be compared.
Simulation
- Simulation of dynamic processes allows for the behavior of a system over time to be calibrated to historical data, evaluated for effectiveness, and tested for ways in which performance metrics can be improved.
Integration of Various Analytical Methods
- Integration of various analytical methods such as regression analysis, logit choice algorithms, and utility functions which flesh out the operational stock/flow model structure with well-established approaches from the world of economic research.
- Current simulation engines such as iThink™ , AnyLogic™ and Vensim® are equipped with powerful optimization routines to provide yet another level of analytical rigor.
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