About Our Approach
What do you mean by data-driven design?
We bring a mix of design thinking and data science to all of our projects. We combine these principles with flexibility in our choice of research methods, tools and frameworks.
What is complexity?
Complexity theory, specifically complex adaptive systems theory, is a very useful framework to analyze highly connected systems. Simple cause and effect frameworks can’t solve problems impacted by underlying conditions like climate change, government policy, cultural divides, or media fragmentation. Complexity gives us a vocabulary and tools to frame problems in the context of systems, understand why simple fixes often fail, and design more effective solutions. We apply principles of complexity to improve the way people share ideas and work together.
Why did you build a technology platform?
When we came up with the idea for Thicket Labs to focus on solving complex social problems, we knew existing social science-based research and design tools were not robust enough to serve our needs. We needed to be able to quickly understand a complex system, analyze the system’s ability to adapt to different conditions, and provide a space for multiple stakeholders to express their opinions without being ignored or disregarded. And we needed to be able to do all of these things quickly, efficiently, and at scale. When we discovered fuzzy cognitive mapping, the model fell into place. We built Thicket's technology platform to power our complex problem solving process. Thicket is a collaborative intelligence technology platform that specializes in mapping and modeling people problems. Thicket powers Thicket Lab's projects, but is also a standalone SaaS product for companies and organizations to use to manage their own complex networks. Get in touch to learn more.
What is fuzzy cognitive mapping?
Thicket developed our initial technology capabilities using a methodology called fuzzy cognitive mapping. First proposed in 1985 by Bart Kosko of USC, fuzzy cognitive maps are a mashup of two frameworks: cognitive maps and fuzzy logic. Cognitive maps, also called mental maps or conceptual maps, were first identified by early behavioral psychologist Edward Tolman in 1948 to explain how people (well, rats, actually) understand and remember spatial environments. Cognitive maps are now used across a wide variety of fields as a tool for people to express how they think about any environment, socially constructed or physically located. Cognitive maps are recorded as visual drawings using circles to document concepts and lines and arrows to show how those concepts relate to each other. Fuzzy logic is a branch of mathematics that accommodates approximate answers rather than forcing a precise result. Fuzzy logic can handle situations in which we don’t know all the variables at play, or a system in which there are multiple truths, not only a single truth. This makes fuzzy logic ideal for managing complex information systems in which we need to analyze factors leading to multiple possible outcomes. By combining individual cognitive maps using fuzzy logic, we can build a picture of a complex system — a picture that is far richer than any single person’s powers of observation can reveal. This is how we map complex social systems.
How do you analyze a fuzzy cognitive map?
When we integrate individual cognitive maps using fuzzy logic, the result is a map so complex that visually, it means very little to the human eye. In order to make sense of it, we have to analyze it using several different methods. We can use network analysis to understand properties about the system, such as how it’s shaped, whether there are areas where there is a concentration of relationships, and more. These properties can tell us much about how the system behaves at the macro level. We can use data modeling to run scenarios to see how the system may respond or adapt to a specific situation. By changing variables within the system, we can model hypothetical decisions and evaluate how those decisions will be received. We are exploring how to apply these different methods, and what the results mean in the context of social systems specifically.