Leveraging artificial intelligence (AI) concepts and technologies similar to those that power leading business and scientific applications, we are an experienced group of Canadian experts now forging an innovative way to design, manage, measure and evaluate interventions and to tackle complex policy problems. We believe that better intervention causal models are the key to evidence-based learning and improved outcomes. We help our clients create these models from multiple evidence streams, including scientific literature, deliberative stakeholder narrative, expert knowledge and open/big data. By integrating these data to tame complexity, we enable our clients to construct powerful visualizations of how their systems work. Our methods and products further support adaptive, long-term learning by providing the means to test intervention models using the right information.
It is our view that complex intervention realities, rapid change, and the need to be more accountable, efficient and effective with limited resources, all point to a pressing need to use the best and most innovative tools to solve both new and persistent problems. With the approach we now bring to the community, we feel that organizations no longer have to make a choice between rigour and clarity. Our purpose and mission is to help organizations leverage cutting edge tools to identify the detailed intervention causal structures that drive behavior and change, ultimately supporting more credible strategic design, more informed decision-making and better outcomes.
An Approach to Understanding Virtually Any Intervention System
One of the advantages of our approach is that it can be applied to any system where understanding cause and effect is important. Knowledge of the cause-effect relationships among key system drivers is a critical prerequisite for the success of virtually all policies and programs.
A few examples of areas where our approach can be applied include:
Why Use Evidence?
An intervention or decision informed by evidence:
- Is more likely to achieve desired outcomes - Ensures resources are directed to the right activities, increasing efficiency - Builds trust and buy-in among stakeholders - Increases institutional credibility - Decreases litigation exposure - Satisfies the increasing demand for evidence-informed decision-making
Our approach involves two complementary, mutually reinforcing innovations:
High-level summary of our process for generating intelligent insight into how policies and programs work, and for determining optimal points of intervention:
Chief Executive Officer Founder
A native Montréaler, Chris studied math, chemistry and physics at Dawson College in the mid-80's, and went on to earn earned a B.Sc. from McGill in natural history and wildlife science, and an M.Sc. from the University of Ottawa, with a focus on how human activities affect natural ecosystems. Chris then entered the Canadian federal public service, where he took on various positions related to environmental toxicology, ecosystem management, sustainable development and environmental auditing. It was during his time with the Auditor General of Canada that Chris became interested in the policies and frameworks associated with measuring organizational performance and accountability. In 2006, Chris opened his own consulting firm specializing in program evaluation. As he gained insight into some of the challenges associated with program evaluation theory and practice, he observed what he sees as serious gaps in how interventions are designed and measured. Chris is now concerned with developing better means to understand how interventions work, how to improve design processes, and how to support evidence-informed decision-making. His vision is a world in which decision-making is systematically supported with evidence, including research, expert knowledge, stakeholder narrative, and open/big data. To realize this vision, he is centering his focus on the use of cutting-edge concepts and technologies, including those associated with artificial intelligence, to gather, analyze and test the evidence needed to bring policy-making into the modern, data-driven era.
Matthew Spencer is PolicySpark’s Principal Scientist. Matt is an applied scientist with expertise in enterprise-scale machine learning, software development, data science, and computational biology. He is the engine behind PolicySpark’s innovative technological solutions. He obtained a BASc. in computer engineering from the University of Ottawa and went on to earn a Ph.D. in cybernetics from the University of Reading in the United Kingdom. The focus of his thesis was complex network models of brain connectivity. Matt is an effective international and inter-disciplinary collaborator. He has over 10 years of experience working with teams around the world and communicating complex ideas to a wide range of audiences. He has 5 years of experience leading machine learning projects at Amazon. His technical expertise includes machine learning research and development employing a wide variety of technologies, including TensorFlow, Sklearn, Spark, Python and AWS. Other strengths include natural language processing, biosignal/time-series analysis, complex network analysis (Graph Theory), predictive stochastic models, reinforcement learning, programming (Python, Java, C#, C/C++, MATLAB), and enterprise software architecture.
To get in touch, call us directly at +1.613.295.2870 or send us a message using the form below. Alternatively, we can be reached at info[at]policyspark.ai
It would be our pleasure to discuss your organizational needs, and how our solutions can help you achieve your strategic goals.