Andreas Fischer

The Process of Solving Complex Problems

This article is about Complex Problem Solving (CPS), its history in a variety of research domains (e.g., human problem solving, expertise, decision making, and intelligence), and a formal definition and a process theory of CPS applicable to the interdisciplinary field. CPS is portrayed as (a) knowledge acquisition and (b) knowledge application concerning the goal-oriented control of systems that contain many highly interrelated elements (i.e., complex systems). The impact of implicit and explicit knowledge as well as systematic strategy selection on the solution process are discussed, emphasizing the importance of (1) information generation (due to the initial intransparency of the situation), (2) information reduction (due to the overcharging complexity of the problem’s structure), (3) model building (due to the interconnectedness of the variables), (4) dynamic decision making (due to the eigendynamics of the system), and (5) evaluation (due to many, interfering and/or ill-defined goals).

Assessing complex problem-solving skills with multiple complex systems

In this paper we propose the multiple complex systems (MCS) approach for assessing domain-general complex problem-solving (CPS) skills and its processes knowledge acquisition and knowledge application. After defining the construct and the formal frameworks for describing complex problems, we emphasise some of the measurement issues inherent in assessing CPS skills with single tasks (i.e., fixed item difficulty, low or unknown reliability, and a large impact of random errors). With examples of the MicroDYN test and the MicroFIN test (two instances of the MCS approach), we show how to adequately score problem-solving skills by using multiple tasks. We discuss implications for problem-solving research and the assessment of CPS skills in general.

Complex problem solving—single ability or complex phenomenon?

In recent years, large scale assessments such as PISA (OECD, 2014) have revived the interest in complex problem solving (CPS). In accordance with the constraints of such assessments, the focus was narrowed to psychometric aspects of the concept, and the minimal complex systems test MicroDYN has been propagated as an efficient instrument for measuring individual differences in CPS (Wustenberg et al., 2012; Greiff et al., 2013). At present, MicroDYN is the most common psychometric instrument claiming to measure CPS1. MicroDYN consists of a number of linear systems with mostly three input and three output variables. The subjects have to explore each system, enter their insights into a causal diagram (representation phase) and subsequently steer the system to a given array of target values by entering input values (solution phase). Each system is attended to for about 5 min. MicroDYN yields reliable measures.

Common process demands of two complex dynamic control tasks : Transfer is mediated by comprehensive strategies

Although individual differences in complex problem solving (CPS) are well–established, relatively little is known about the process demands that are common to different dynamic control (CDC) tasks. A prominent example is the VOTAT strategy that describes the separate variation of input variables (“Vary One Thing At a Time”) for analyzing the causal structure of a system. To investigate such comprehensive knowledge elements and strategies, we devised the real-time driven CDC environment Dynamis2 and compared it with the widely used CPS test MicroDYN in a transfer experiment. One hundred sixty five subjects participated in the experiment, which completely combined the role of MicroDYN and Dynamis2 as source or target problem. Figural reasoning was assessed using a variant of the Raven Test. We found the expected substantial correlations among figural reasoning and performance in both CDC tasks. Moreover, MicroDYN and Dynamis2 share 15.4% unique variance controlling for figural reasoning. We found positive transfer from MicroDYN to Dynamis2, but no transfer in the opposite direction. Contrary to our expectation, transfer was not mediated by VOTAT but by an approach that is characterized by setting all input variables to zero after an intervention and waiting a certain time. This strategy (called PULSE strategy) enables the problem solver to observe the eigendynamics of the system. We conclude that for the study of complex problem solving it is important to employ a range of different CDC tasks in order to identify components of CPS. We propose that besides VOTAT and PULSE other comprehensive knowledge elements and strategies, which contribute to successful CPS, should be investigated. The positive transfer from MicroDYN to the more complex and dynamic Dynamis2 suggests an application of MicroDYN as training device.