Go Through Our Case Studies Before Hiring Services
RWISE Information Synthetic Environment is a leading-edge Software as a Service, rich in low-touch/no-touch Data Science, Machine Learning, Artificial Intelligence tools, and techniques to give deep and augmented analytics. It is married to an Agent-based simulation engine using artificial intelligence to forecast impacts to actions you want to test forward in time. The agent-based approach captures behavioral changes that are likely, based on individual well-being and human nature’s drive to achieve or maintain certain expectations. It is designed to allow the new phenomenon to emerge during simulations, thus avoiding the "backward looking" shortfalls of most modeling techniques used today.
RWISE for Equitable Education LUMINA Foundation
PROBLEM: Increase post-secondary education of minority groups.
ACTION: Modeled US population demographically down to census tract; tested equitable post-secondary attainment policies; included use of social media; forecasted long-term impact out to 2040.
RESULT: Found post-secondary attainment must include industry certifications as a means to bring the population to a level of 60% attainment and help fill the inequity gap for low-income minority groups.
SUBSTANTIATION: We’ve been approached to perform similar modeling and simulation for the post-COVID world with increased granularity.
RWISE for Criminal Justice Reform City of Indianapolis
PROBLEM: Reduce incarcerations & incarceration durations; eliminate inequality in the treatment of low-income/minority residents.
ACTION: Simulated current criminal justice process, proposed mental health interventions, response and ingestion alternatives, & new justice system off-ramps.
RESULT: Quantified effectiveness of multi-pronged interventions; found alternatives through increased mental health resources, response capabilities, treatment pathways, reduced incarcerations, & durations.
SUBSTANTIATION: Opened new discussion on expanding resident well-being initiatives tested with RWISE.
RWISE for Shaping Labor Market Kingdom of Saudi Arabia
PROBLEM: Design effective policies to re-engage Saudi citizens in non-oil manufacturing industries.
ACTION: Simulated all planned policies, supplemented by policies addressing all aspects of well-being.
RESULT: Only policies involving Freedom of Movement had an impact.
SUBSTANTIATION: McKenzie performed 1-year study confirming simulation results; KSA provided women the right to drive.
Potential Use Cases:
RWISE for Shaping Party Planks Political Think Tanks and Strategists
PROBLEM: Governmental policies create tradeoffs between well-being facets that often go overlooked by party leaders.
ACTION: Simulated planned policies, supplemented by policies addressing all aspects of well-being.
RESULT: Look at wellbeing facets at the census track level with demographical granularity.
SUBSTANTIATION: We have already modeled education, economics, labor, and basic needs.
RWISE for Health Improvement Insurance / Health Industries
PROBLEM: Health-related behaviors comprise the greatest impacts on health costs and wellbeing.
ACTION: Simulated current health care resources, supporting infrastructures, environmental factors, and health demographics.
RESULT: Integrated model of health-related behaviors by demographics overlaying the health care infrastructure to visualize care desserts and behavior-driven requirements.
SUBSTANTIATION: Opening new discussions on modeling health behavior improvement strategies with initiatives tested with RWISE.
Developing Strategies for the Enterprise
Bring in the data to build out your competative environment with all the different players, the economy, your industry, your markets and the institutions that set the rules. We create an integrated synthetic environment capturing the nuances and interdependencies. Test your startegic actions in our simulation engine to forecast primary, secondary and tertiary effects to your environment. Then see how your competators are likely to react. Forecast how long and how significantly your actions will impact your business environment. Scrape new data to keep your synthetic model fresh and up-to-date.