Goal setting, evaluation, learning and revision: A dynamic modeling approach

Barlas, Yaman; Yasarcan, Hakan

doi:10.1016/j.evalprogplan.2005.08.003

Cited by 38 publications

(33 citation statements)

References 9 publications

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“…For behavioral decision heuristics, see for example [25,42,43]. c The fourth simplified game is obtained from the original game by changing the dynamic ''Shipment Capacity'' to a constant and setting it equal to 30 items/week.…”

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confidence: 99%

Improving understanding, learning, and performances of novices in dynamic managerial simulation games

Yasarcan

2009

Complexity

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Simulations are increasingly used in training and education because of their success and their advantages as a learning method. However, it has also been observed that the dynamic complexity of simulations creates learning difficulties, and that performance tends to plateau quickly at a level well below benchmark performance. To overcome this difficulty, a gradual-increase-in-complexity approach is proposed, which suggests developing simpler versions of a simulation game that can be used as part of the training. Accordingly, the authors developed a series of inventory-management simulations and conducted an experiment. The results indicate an improvement in the success of the inventory-management simulation as a training tool. 2009 Wiley Periodicals, Inc. Complexity 15: 31-42, 2010

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Improving understanding, learning, and performances of novices in dynamic managerial simulation games

Yasarcan

2009

Complexity

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“…In this situation, management has two options. They can either adjust the long‐term goal to be closer to the performance achieved by the short‐term actions, or they can apply a longer‐term fix that targets the cause of the gap (see Barlas and Yasarcan, , , for a comprehensive model of goal dynamics in organizations). In the context of failing to achieve long‐term goals, it is hypothesized that management will adjust the long‐term goal downward to be closer to actual performance (i.e., the loop representing the symptomatic fix dominates; Braun, ).…”

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confidence: 99%

A co-flow structure for goal-directed internal change

et al. 2017

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We describe a co-flow structure that models internal, goal-directed changes to an attribute (e.g., employee loyalty) of fundamental material (e.g., employees). This co-flow accommodates problems not adequately modeled with an existing, generic structure. Our structure builds on the co-flow proposed by Hines, which uses an information delay to model external change to an attribute. We use a first-order information delay to model both external changes to the attribute from the material stock and internal changes from an internal goal for the attribute. We provide an exact, dynamic solution for this co-flow enabling us to precisely describe its equilibrium and non-equilibrium behavior. Several examples are provided and discussed, including a situation where a management program is designed to increase average employee loyalty. In addition, we review applications of traditional and Hines co-flow structures to provide background and to describe our evolutionary path towards design of the new co-flow.

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“…For example, with respect to the difficulty of the goal, individuals might pursue sequential goals, which differ in their level of difficulty: they might function well with less challenging goals if they perceive the accomplishment of these goals as the basis for pursuing more difficult and challenging goals in the future (Fried & Slowik, 2004). Another example relates to the concept of floating goals (see Barlas & Yasarcan, 2006), a more sophisticated and realistic goal-related structure in which the difficulty of the goal is adjusted depending on current conditions (e.g., if the performance of an individual is persistently poor in approaching the original goal, then he/she lowers the goal, but if he/she exhibits surprisingly good performance, then the goal is pushed higher). These new approaches raise questions concerning the optimal sequence of goals, and the way in which the goals should be adjusted over time in order to maximize long-term performance.…”

Section: Discussionmentioning

confidence: 99%

Experimental design and primary data analysis methods for comparing adaptive interventions.

Nahum‐Shani

Qian²,

Almirall

et al. 2012

Psychological Methods

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In recent years, research in the area of intervention development is shifting from the traditional fixed-intervention approach to adaptive interventions, which allow greater individualization and adaptation of intervention options (i.e., intervention type and/or dosage) over time. Adaptive interventions are operationalized via a sequence of decision rules that specify how intervention options should be adapted to an individual’s characteristics and changing needs, with the general aim to optimize the long-term effectiveness of the intervention. Here, we review adaptive interventions, discussing the potential contribution of this concept to research in the behavioral and social sciences. We then propose the sequential multiple assignment randomized trial (SMART), an experimental design useful for addressing research questions that inform the construction of high-quality adaptive interventions. To clarify the SMART approach and its advantages, we compare SMART with other experimental approaches. We also provide methods for analyzing data from SMART to address primary research questions that inform the construction of a high-quality adaptive intervention.

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Goal setting, evaluation, learning and revision: A dynamic modeling approach

Cited by 38 publications

References 9 publications

Improving understanding, learning, and performances of novices in dynamic managerial simulation games

Improving understanding, learning, and performances of novices in dynamic managerial simulation games

A co-flow structure for goal-directed internal change

Experimental design and primary data analysis methods for comparing adaptive interventions.

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