Optimization as an analysis tool for human complex decision making

We present a problem class of mixed-integer nonlinear programs (MINLPs) with nonconvex continuous relaxations which stem from economic test scenarios that are used in the analysis of human complex problem solving. In a round-based scenario participants hold an executive function. A posteriori a performance indicator is calculated and correlated to personal measures such as intelligence, working memory, or emotion regulation. Altogether, we investigate 2088 optimization problems that differ in size and initial conditions, based on real-world experimental data from 12 rounds of 174 participants. The goals are twofold. First, from the optimal solutions we gain additional insight into a complex system, which facilitates the analysis of a participant’s performance in the test. Second, we propose a methodology to automatize this process by providing a new criterion based on the solution of a series of optimization problems. By providing a mathematical optimization model and this methodology, we disprove the assumption that the “fruit fly of complex problem solving,” the Tailorshop scenario that has been used for dozens of published studies, is not mathematically accessible—although it turns out to be extremely challenging even for advanced state-of-the-art global optimization algorithms and we were not able to solve all instances to global optimality in reasonable time in this study. The publicly available computational tool Tobago [TOBAGO web site https://sourceforge.net/projects/tobago] can be used to automatically generate problem instances of various complexity, contains interfaces to AMPL and GAMS, and is hence ideally suited as a testbed for different kinds of algorithms and solvers. Computational practice is reported with respect to the influence of integer variables, problem dimension, and local versus global optimization with different optimization codes

Optimization-based Analysis and Training of Human Decision Making

In the research domain Complex Problem Solving (CPS) in psychology, computer-supported tests are used to analyze complex human decision making and problem solving. The approach is to use computer-based microworlds and to evaluate the performance of participants in such test-scenarios and correlate it to certain characteristics. However, these test-scenarios have usually been defined on a trial-and-error basis, until certain characteristics became apparent. The more complex models become, the more likely it is that unforeseen and unwanted characteristics emerge in studies. In this thesis,we use mathematical optimization methods as an analysis and training tool for Complex Problem Solving, but also show how optimization should be used in the design stage of new complex problem scenarios in the future. We present the IWR Tailorshop, a novel test scenario with functional relations and model parameters that have been formulated based on optimization results. The IWR Tailorshop is the first CPS test-scenario designed for the application of optimization and is based on the economic framing of another famous microworld, the Tailorshop. We describe an optimization-based analysis approach and extend it to optimization-based feedback with different approaches for both feedback computation and feedback presentation. Additionally, we investigate differentiable reformulations for an unavoidable minimum expression and show the according numerical results. To address the difficulties of computing globally optimal solutions for this test-scenario, which yields non-convex mixed-integer optimization problems, we present a decomposition approach for the IWR Tailorshop. The new test-scenario has been implemented in a web-based interface together with an analysis software for collected data, which both are available as open-source software and allow for an easy adaption to other test-scenarios. In this work, we also apply our methodology in a web-based feedback study using the IWR Tailorshop in which participants are trained to control the microworld by optimization-based feedback. In this study with 148 participants, we show that such a feedback can significantly improve participants’ performance in a complex microworld with a possibly huge difference to a control group. However, the performance improvement depends on the representation of the feedback. We give a detailed analysis of the study and report on new insights about human decision making which only have been possible through the IWR Tailorshop and our optimization-based analysis and training approach

A Decomposition Approach for a New Test-Scenario in Complex Problem Solving

Over the last years, psychological research has increasingly used computer-supported tests, especially in the analysis of complex human decision making and problem solving. The approach is to use computer-based test scenarios and to evaluate the performance of participants and correlate it to certain attributes, such as the participant's capacity to regulate emotions. However, two important questions can only be answered with the help of modern optimization methodology. The first one considers an analysis of the exact situations and decisions that led to a bad or good overall performance of test persons. The second important question concerns performance, as the choices made by humans can only be compared to one another, but not to the optimal solution, as it is unknown in general.\ud \ud Additionally, these test-scenarios have usually been defined on a trial-and-error basis, until certain characteristics became apparent. The more complex models become, the more likely it is that unforeseen and unwanted characteristics emerge in studies. To overcome this important problem, we propose to use mathematical optimization methodology not only as an analysis and training tool, but also in the design stage of the complex problem scenario.\ud \ud We present a novel test scenario, the IWR Tailorshop, with functional relations and model parameters that have been formulated based on optimization results. We also present a tailored decomposition approach to solve the resulting mixed-integer nonlinear programs with nonconvex relaxations and show some promising results of this approach

Behavioral conservatism is linked to complexity of behavior in chimpanzees (<i>Pan troglodytes</i>):implications for cognition and cumulative culture

Cumulative culture is rare, if not altogether absent in nonhuman species. At the foundation of cumulative learning is the ability to modify, relinquish, or build upon previous behaviors flexibly to make them more productive or efficient. Within the primate literature, a failure to optimize solutions in this way is often proposed to derive from low-fidelity copying of witnessed behaviors, suboptimal social learning heuristics, or a lack of relevant sociocognitive adaptations. However, humans can also be markedly inflexible in their behaviors, perseverating with, or becoming fixated on, outdated or inappropriate responses. Humans show differential patterns of flexibility as a function of cognitive load, exhibiting difficulties with inhibiting suboptimal behaviors when there are high demands on working memory. We present a series of studies on captive chimpanzees that indicate that behavioral conservatism in apes may be underlain by similar constraints: Chimpanzees showed relatively little conservatism when behavioral optimization involved the inhibition of a well-established but simple solution, or the addition of a simple modification to a well-established but complex solution. In contrast, when behavioral optimization involved the inhibition of a well-established but complex solution, chimpanzees showed evidence of conservatism. We propose that conservatism is linked to behavioral complexity, potentially mediated by cognitive resource availability, and may be an important factor in the evolution of cumulative culture.</p

Integrated Optimization of Mars Hybrid Solar-Electric/Chemical Propulsion Trajectories

NASAs Human Exploration and Operation Mission Directorate is developing a reusable hybrid transportation architecture in which both chemical and solar-electric propulsion systems are used to deliver crew and cargo to the Martian sphere of influence. By combining chemical and solar-electric propulsions into a single spacecraft and applying each where it is the most effective, the hybrid architecture enables a series of Mars trajectories that are more fuel efficient than an all chemical propulsion architecture without significant increase to trip time. Solving the complex problem of low-thrust trajectory optimization coupled with the vehicle sizing requires development of an integrated trajectory analysis frame- work. Previous studies have utilized a more segmented optimization framework due to the limitation of the tools available. A new integrated optimization framework was recently developed to address the deficiencies of the previous methods that enables higher fidelity analysis to be performed and increases the efficiency of large design space explorations

OptBPPlanner: Automatic Generation of Optimized Business Process Enactment Plans

Unlike imperative models, the specifi cation of business process (BP) properties in a declarative way allows the user to specify what has to be done instead of having to specify how it has to be done, thereby facilitating the human work involved, avoiding failures, and obtaining a better optimization. Frequently, there are several enactment plans related to a specifi c declarative model, each one presenting specifi c values for different objective functions, e.g., overall completion time. As a major contribution of this work, we propose a method for the automatic generation of optimized BP enactment plans from declarative specifi cations. The proposed method is based on a constraint-based approach for planning and scheduling the BP activities. These optimized plans can then be used for different purposes like simulation, time prediction, recommendations, and generation of optimized BP models. Moreover, a tool-supported method, called OptBPPlanner, has been implemented to demonstrate the feasibility of our approach. Furthermore, the proposed method is validated through a range of test models of varying complexity.Ministerio de Ciencia e Innovación TIN2009-1371

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table