Lewicki is best known for his research on nonconscious information processing and self-perpetuation, where he demonstrated that procedural knowledge is created via nonconscious acquisition of information about complex covariations between events or features, and that nonconsciously, individuals can acquire very complex knowledge structures based on highly-multidimensional patterns of data. Lewicki's research on the self-perpetuating development of encoding dispositions purports to show that accidentally acquired cognitive preferences or other encoding/interpretive dispositions can gradually develop and strengthen in a self-perpetuating manner. This mechanism may contribute to the development of s and individual preferences, it may facilitate learning, but it can also lead to self-perpetuating development of dysfunctional biases, phobias, aversions and other symptoms of personality disorders.
Lewicki together with fellow cognitive researcher Thomas Hill were among the first to publish evidence that advanced expertise acquired by humans via experience, involves the acquisition and utilization of repeatable patterns in data that are structurally more complex than what humans can verbalize or intuitively experience, because they involve high-order interactions between multiple variables. The human consciousness usually cannot handle more than only third-order interactions. This approach became popular and rapidly adopted by the corporate world as so-called “predictive data mining” starting in the late 1990s and StatSoft’s STATISTICA Data Miner is now one of the widely used enterprise-level software systems for data mining and predictive analytics. Lewicki began to write data analysissoftware for personal computers around 1984. He found the mainframe data analysis software difficult to use and needed a tool to analyze his research data. He gave this software away for free to colleagues, who provided positive feedback and this led him to incorporate StatSoft in 1984. StatSoft then expanded into enterprise-scale predictive analytics software and, according to the , is now an international analytic solutionsoftware provider with more than 1,000,000 users and 30 offices worldwide.
StatSoft
Lewicki was the Chief Executive Officer and President of StatSoft; on March 24, 2014, StatSoft was acquired by Dell. In a recent interview, Lewicki emphasized that StatSoft’s corporate culture remains focused on long-term research and altruism rather than profits: "Our mission is to create value and to make the world a better place, and analytics contribute directly to that. Our employees' biggest reward is to see that what we're doing works and is making a difference." Lewicki is the co-author of Statistics: Methods and Applications is a popular resource on statistics and predictive modeling on the web with over 375,000 links from other websites worldwide. At StatSoft, Lewicki also directed research sponsored by the Electric Power Research Institute of Palo Alto, to demonstrate the effectiveness of pattern recognition and predictive modeling algorithms for the optimization of combustion processes, to reduce harmful emissions from fossil fuel plants. The results of this research were published as EPRI Report number 1016494, and were also presented at the 2008 Meeting of the Asia-Pacific Partnership on Clean Development and Climate. Lewicki has communicated to EPRI that because of the relevance of this work for environmentally sustainable power generation, this technology will be released to the public domain as soon as the proper regulatory infrastructure is established by the EPA and DOE.
Involvement in European economies
In the fall of 2012, Lewicki led the “Free Enterprise Software for Struggling European Economies” initiative, where StatSoft is installing free enterprise systems to companies in Greece, Portugal, and Spain to boost their productivity and competitiveness, and to facilitate their recovery. Lewicki has directed an urging them to join the initiative.
After the sale of StatSoft to Dell in 2014, Lewicki focused on applications of Artificial Intelligence in Medicine, that has been traditionally slower in adopting big data learning and data mining, and he co-founded and .
