Revolutionary new technology from Evolving Logic

Evolving Logic's Robust Adaptive Planning™ (RAP™) had its genesis in ten years of scholarly and policy work, primarily at RAND. Here is a collection of essays and journal articles which describe the theoretical basis of RAP™ and the CAR™ (Computer Assisted Reasoning®) technology that power our revolutionary new tools.

Computer Assisted Reasoning®
CAR™, based on exploratory modeling methods in the scholarly literature, is an approach to human-computer interaction that supports decision-making under conditions of deep uncertainty.

Robust Adaptive Planning™
When faced with deep uncertainty, managers often respond with flexibility and by being adaptive. RAP™ software can help decision-makers systematically find adaptive-decision strategies robust across a wide range of uncertainty. These papers describe how, focusing on the public policy problem of global climate change.

Portfolio Problems:
In real life situations, decision-makers faced with extreme uncertainty should look for robust solutions, rather than a hypothetical "best" optimized for a particular set of assumptions. That is, they recognize that in a fast-changing, uncertain environment, the best can be the enemy of the good. What is required is a means to find "satisficing" solutions systematically. This case illustrates this approach applied to a portfolio problem.

Agent-Based Models:
Agent-based models are becoming increasingly popular in business simulation because they provide a means to incorporate important information, such as imperfect information or the diversity of preferences among consumers, that are poorly treated in more traditional modeling formalisms. However, agent-based models are difficult to employ within traditional decision-analysis frameworks and are often relegated to building intuition about a problem or "flight simulator" approaches, rather than rigorous analysis. CARs™ provides a systematic means to extract the information contained in agent-based models.

Scenario-Based Planning:
RAP™ combines the best features of qualitative scenario-based planning and traditional quantitative forecasting.

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