Mental Modeling Technology (MMT)

Effective risk communication requires contributions from subject matter experts, who know the issues; analysts, who can identify the essential ones; behavioral scientists, who can address audience members’ information needs; and specialists, who can create channels for trusted two-way communication between the parties. The mental models approach provides a framework for organizing the information needed to accomplish this task. However, it takes deep personal and organizational commitment to bring and keep the parties together. Mental Modeling shows how to make that happen, integrating theory and practice.
The range of its applications is remarkably broad, including plastic surgery, climate change, dairy farming, deep mining, biosolids, nuclear power, and carbon capture and sequestration. So is the range of stakeholders and audiences, including physicians, patients, regulators, laborers, engineers, land use planners, and river managers. And, so are the methods, including community workshops, in-depth interviews, expert elicitation sessions, computer models, worker training, and broad and narrowband communication. These ranges of topics, audiences, and method show the generality of the approach and the creativity of the authors in its use.
Readers of Mental Modeling will acquire an understanding of the theory underlying the approach, with its basic principles illustrated in diverse, practical examples. Readers will learn methods that they can apply directly and strategies for generating their own. And they will come away with an appreciation of the diligence needed to create communications worthy of the stakes riding on them. Although not easy, the work is exciting — and gratifying.

Baruch Fischhoff, PhD
Pittsburgh, PA

Planned Innovation Approach (PI)

Planned Innovation is a structured approach for turning ideas, needs, opportunities, and emerging technologies into practical, evidence-based decisions. Like Mental Modeling Technology, it requires contributions from many kinds of participants: subject matter experts, who understand the technical possibilities; users and stakeholders, who understand needs and constraints; analysts, who can structure the problem and compare alternatives; managers and sponsors, who can mobilize resources; and communication specialists, who can support shared understanding and implementation. The approach provides a framework for organizing these contributions so that innovation is not left to inspiration alone, but becomes a disciplined process of exploration, evaluation, selection, and learning.
Planned Innovation begins with the recognition that innovations succeed only when technical feasibility, user value, organizational readiness, economic justification, and social acceptance are considered together. It helps teams define the opportunity, clarify objectives, identify stakeholders, elicit expert and user knowledge, generate alternatives, assess uncertainties, compare options, and plan implementation. In this sense, it can be seen as a natural companion to Mental Modeling Technology: MMT helps reveal how experts and audiences understand a problem, while Planned Innovation uses such understanding to guide the development, prioritization, and deployment of new solutions.
The range of its applications is broad, including product and service development, technology commercialization, healthcare innovation, energy systems, infrastructure planning, public policy, organizational transformation, and risk-informed decision making. So is the range of methods that can support it, including expert elicitation, interviews, surveys, workshops, scenario analysis, portfolio analysis, multi-criteria decision analysis, cost–benefit analysis, prototyping, pilot testing, and communication planning. These methods allow qualitative judgments and quantitative evidence to be brought together without forcing one to replace the other.
Practitioners of Planned Innovation learn how to move from a promising idea to a defensible course of action. They learn how to structure innovation problems, identify what is known and unknown, compare competing concepts, define success criteria, and create implementation pathways that can be tested and revised. Although the process requires commitment, openness, and analytical discipline, it helps organizations make innovation more transparent, participatory, and accountable. Used together with Mental Modeling Technology, Planned Innovation can support not only better communication about complex problems, but also better design of the innovations intended to solve them.

OpenAI ChatGPT 5.5

Cognitive Analysis Software Suite (CASS)

The Cognitive Analysis Software Suite (CASS) that is specifically designed to efficiently support the unique empirical methods embodied in the research component of MMT from developing graphical depictions of the systems being modeled, through coding, analysis of qualitative data, back to graphical depiction of research results. CASS was developed to enable researchers to hypothesize, visualize, tabulate, analyze, and report on individuals’ mental models of complex social and technical issues. It provides the analytical framework for conducting and analyzing mental models research.

Matthew D. Wood, Sarah Thorne, Daniel Kovacs, Gordon Butte, Igor Linkov
Mental Modeling Approach — Risk Management Application Case Studies, Springer