CHS: Small: Distributed Analogical Innovation

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This social computing research will build a scientific foundation for distributed analogical innovation that integrates cognitive science, social science, design, and computer science. The results will contribute back to theory in these source disciplines as well, including theories of analogy, coordination, distributed innovation, scientific discovery, crowdsourcing, and collective intelligence. Innovation in science and technology is often driven by analogy, and opportunities for finding fruitful analogies are exploding with the increased online availability of repositories of ideas ranging from scientific papers to product ideas to the diverse text and video resources. However, our ability to process this deluge of information to find and use analogies is severely bottlenecked by individual cognitive limits, as the speed and capacity with which individuals can learn and explore new domains have not kept up with the rapid growth in online information from which analogies can be discovered. Furthermore, people are hampered by cognitive biases that prevent them from seeing and applying important parallels to solve comparable problems in different domains. Instead of relying on a single individual to find and apply an analogy, this research will develop methods for distributing analogical processing across multiple individuals. The development of improved innovation processes could accelerate technological innovation and scientific discovery, with corresponding benefits to the economy and to science.The general approach will be to conduct empirical studies to rigorously explore and characterize the benefits and limitations of distributed analogical innovation, and then to generalize these findings in the context of two real-world innovation communities. Distributing innovation tasks to different individuals could have a number of significant advantages. First, increasing the number of people involved could increase the capacity to find and use analogies across many domains. Second, changing the representations of problems and solutions could help innovators see the relevance of solutions from distant domains and reduce problems with fixation, in which people are overly influenced by surface features. Third, by distributing the steps involved in analogy to different people, alternative innovation paths could be explored effectively in parallel. Fourth, disaggregating the innovation pipeline opens up participation to many more people; one need not be an expert in a problem domain, for example, to find analog solutions in remote domains. Fifth, in contrast to innovation contests that reward only a small number of contest winners, the sequential method to be used in this research allows people to build on each other's work and doesn't waste the labor of the vast majority of participants.