Integrated Science Lab
“Everyone designs who devises courses of action aimed at changing existing situations into preferred ones. The intellectual activity that produces material artifacts is no different fundamentally from the one that prescribes remedies for a sick patient or the one that devises a new sales plan for a company or a social welfare policy for a state" —Herb Simon, Sciences of the Artificial (1969)
“All that we do, almost all the time, is design, for design is basic to all human activity. The planning and patterning of any act towards a desired, foreseeable end constitutes the design process. Any attempt to separate design, to make it a thing-by-itself, works counter to the inherent value of design as the primary underlying matrix of life. . . . Design is the conscious effort to impose meaningful order.” --- Victor Papanek, Design for the Real World: Human Ecology and Social Change (1971)
Overview
The Integrated Science Lab seeks to develop collaborative design methods that enable creative designers, cognitive scientists, and technology developers to work together on the design of advanced visual analytics and decision intelligence systems. Our integrated science approach advances the science of human cognition by testing visually-enabled reasoning in interactive graphical information environments. Our application work draws on theory and methods from distributed cognition, visual cognition, and cognitive anthopology to design interactive graphical decision support systems for applications in health, safety, and food systems.
What is Integrated Science?
Analysis and decision-making in today’s highly complex environment often requires the consensus of multiple stakeholders with different perspectives and areas of expertise working with massive datasets. This is as true for technology design teams as it is for decision-makers in organizations and society. The integrated science lab is developing collaborative, interdisciplinary, and co-design methods for our own work, using these methods to co-develop methods and supporting technologies to enable organizations to use similar approaches to address their own problems and opportunities.
Cognitive Engineering:
Decision Intelligence (DI) supports innovation in data analysis and decision-making in organizations and the larger community. DI is described as “a new academic discipline concerned with all aspects of selecting between options. It brings together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them.” (Cassie Kozyrkov) Our DI approach is well-suited for situations where computation-based analysis (e.g. statistics, machine learning) must be reconciled with human understanding, values, and ethics for innovative solutions to complex problems and opportunities.
Visual Analytics (VA) "the science of analytical reasoning facilitated by interactive visual interfaces" (Thomas and Cook) is an example of Highly-Integrated Basic and Responsive Research (HIBAR). VA integrates creative design of visualization systems with scientific research in human cognition, communication, and action in perceptually-rich situations to create highly interactive graphical environments that augment human reasoning.
“This science must be built on integrated perceptual and cognitive theories that embrace the dynamic interaction among cognition, perception, and action. It must provide insight on fundamental cognitive concepts such as attention and memory. It must build basic knowledge about the psychological foundations of concepts such as meaning, flow, confidence, and abstraction. To be effective, the science of visual analytics must be developed within the context of the demands of visual analytics systems. This research will be different from and much more than task analysis. It will be an integration of basic research with a specific task domain to create robust and practical results that advance both visual analytics and efforts to understand the fundamental workings of the human mind. Thomas and Cook “Illuminating the path, a R&D Agenda for Visual Analytics” (2004)
Applications:
Medical information systems
Airwise, a lung health risk communication study led by Sonya Cressman
Cubismi,
Molecular You (archived)
Food systems
DSFAS-AI: Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion
Elicitation and impRovement (DECIDE-SMARTER) in collaboration with NCSU and Quantellia.
Immersive Visualization Environments
MDA IDEaS project "3D Visualization for Complex Information Systems"
Collaboration with the Spatial Interface Lab https://www.spatialinterfaceresearchlab.org/ .
Cognitive Science
Expertise in Visually-Enabled Reasoning:
The aim of this project is to better understand the nature of expertise in understanding complex graphical visualization displays. The end goal is to develop a cognitive model to support design of visualization systems that more effectively augment expert cognition. Much of the work explores the possibility of a “Personal Equation of Interaction”, a proposed measure of individual differences in visualization understanding that could be used to customize an interactive visualization system.
Collaborative analysis and decision making:
Pair and group analytic methods examine collaborative decision-making using visualization systems using Clark’s Joint Activity Theory. Pair analytics requires a dyad of participants: one playing the role of Subject Matter Expert (SME) and a second acting as a Visual Analytics Expert (VAE). The VAE plays the role of the “driver” and the SME plays the role of the “navigator” of an interactive visualization environment. The pairing of VAE and SME generates a natural dialog about the data and the analytic process. For example, the SME may use expertise-related schemas to structure her analysis, detect anomalies in the visual representations of the data, suggest alternative analytical paths, and identify the domain-specific value of data patterns/trends found in the analytical session, while the VAE selects appropriate representations for data and supports their interpretation.
There are a number of research products that result from this procedure. First, we expect that the conclusions reached in the analysis will be superior to those that might be generated though traditional analytic methods. Second, both SME and VAE will learn about each other’s domain of expertise through the process of negotiating a common ground of knowledge needed to drive the joint activity of pair analytics. Third, the process of negotiating common ground is itself of interest since it makes explicit reasoning processes of interacting with the visual representations of the data. These research findings are used to improve visualization and interaction design, develop systems tailored for group analysis, and to develop training methods for analysts and interaction designers.
This method was developed in partnership with The Boeing Company to look at analysis of aircraft safety, reliability and maintainability. Funding was provided by the Boeing Company and MITACS through an SFU/UBC ITB grant "Boeing Support for Visual Analytics in Canada".