Raven's Progressive Matrices and similar matrix problems have been used in research and intelligence testing for decades. The matrix problems serve as a nonverbal test of analogical reasoning and are thought to measure analytical intelligence (Carpenter, Just, & Shell, 1990), also known as fluid intelligence (cf. Cattell, 1963). Although these matrix problems have a wide variety of research applications, the relatively small number of matrices in Raven's original sets (108 total;Raven, Court, & Raven, 1998) limits their utility in several domains, including neuroimaging experiments and computational modeling of cognitive processes.Our goal in the present study was to create and characterize a very large set of matrix problems that have properties similar to those of Raven's original matrices. We sought to create the matrix set in a systematic way that would allow researchers to have a great deal of control over the underlying structure, surface features, and difficulty of the matrix problems. This in turn would allow researchers to systematically expand the range of difficulty in their stimulus sets beyond the range provided by the original Raven's matrices. To accomplish these goals, we analyzed the underlying structures in Raven's original Standard Progressive Matrices (SPMs) to determine what types and combinations of relations were used. On the basis of that analysis, we developed software that can use the same underlying patterns to generate large numbers of unique matrix problems using parameters chosen by the researcher. Specifically, the software is designed so that researchers can choose the type, direction, and number of relations in a problem and create any number of unique matrices that share the same underlying structure (e.g., changes in numerosity in a diagonal pattern) but have different surface features (e.g., shapes, colors).Finally, we used the matrix generation software to produce a representative set of matrix problems that cover the range of underlying structures that can be produced by the software. This set of matrices was compared with Raven's SPMs in a norming study. The first goal of the norming study was to compare the difficulty of the generated matrices with the difficulty of the SPMs with the same underlying structure. The second goal was to assess the difficulty of specific structural features within the matrices and the range of problem difficulties that can be produced by the matrix generation software when those features are combined. Analysis of Raven's Progressive Matrix StructuresPrevious studies have analyzed the factors that contribute to the difficulty of Raven and Raven-like matrix problems. Raven's Progressive Matrices is a widely used test for assessing intelligence and reasoning ability (Raven, Court, & Raven, 1998). Since the test is nonverbal, it can be applied to many different populations and has been used all over the world (Court & Raven, 1995). However, relatively few matrices are in the sets developed by Raven, which limits their use in experiments requiring l...

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Participatory modeling has become an important tool in facilitating resource decision making and dispute resolution. Approaches to modeling that are commonly used in this context often do not adequately account for important human factors. Current techniques provide insights into how certain human activities and variables affect resource outcomes; however, they do not directly simulate the complex variables that shape how, why, and under what conditions different human agents behave in ways that affect resources and human interactions related to them. Current approaches also do not adequately reveal how the effects of individual decisions scale up to have systemic level effects in complex resource systems. This lack of integration prevents the development of more robust models to support decision making and dispute resolution processes. Development of integrated tools is further hampered by the fact that collection of primary data for decision-making modeling is costly and time consuming.This project seeks to develop a new approach to resource modeling that incorporates both technical and behavioral modeling techniques into a single decision-making architecture. The modeling platform is enhanced by use of traditional and advanced processes and tools for expedited data capture. Specific objectives of the project are: 1) Develop a proof of concept for a new technical approach to resource modeling that combines the computational techniques of system dynamics and agent based modeling, 42) Develop an iterative, participatory modeling process supported with traditional and advance data capture techniques that may be utilized to facilitate decision making, dispute resolution, and collaborative learning processes, and 3) Examine potential applications of this technology and process.The development of this decision support architecture included both the engineering of the technology and the development of a participatory method to build and apply the technology. Stakeholder interaction with the model and associated data capture was facilitated through two very different modes of engagement, one a standard interface involving radio buttons, slider bars, graphs and plots, while the other utilized an immersive serious gaming interface.The decision support architecture developed through this project was piloted in the Middle Rio Grande Basin to examine how these tools might be utilized to promote enhanced understanding and decisionmaking in the context of complex water resource management issues. Potential applications of this architecture and its capacity to lead to enhanced understanding and decision-making was assessed through qualitative interviews with study participants who represented key stakeholders in the basin.

People responding to high-consequence national-security situations need tools to help them make the right decision quickly. The dynamic, time-critical, and ever-changing nature of these situations, especially those involving an adversary, require models of decision support that can dynamically react as a situation unfolds and changes. Automated knowledge capture is a key part of creating individualized models of decision making in many situations because it has been demonstrated as a very robust way to populate computational models of cognition. However, existing automated knowledge capture techniques only populate a knowledge model with data prior to its use, after which the knowledge model is static and unchanging. In contrast, humans, including our national-security adversaries, continually learn, adapt, and create new knowledge as they make decisions and witness their effect. This artificial dichotomy between creation and use exists because the majority of automated knowledge capture techniques are based on traditional batch machine-learning and statistical algorithms. These algorithms are primarily designed to optimize the accuracy of their predictions and only secondarily, if at all, concerned with issues such as speed, memory use, or ability to be incrementally updated. Thus, when new data arrives, batch algorithms used for automated knowledge capture currently require significant recomputation, frequently from scratch, which makes them ill suited for use in dynamic, timecritical, high-consequence decision making environments. In this work we seek to explore and expand upon the capabilities of dynamic, incremental models that can adapt to an ever-changing feature space.4

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