Sensitivity analysis of agent-based models: a new protocol

Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions.

The Spread of Ideas in a Network—The Garbage-Can Model

The main goal of our work is to show how ideas change in social networks. Our analysis is based on three concepts: (i) temporal networks, (ii) the Axelrod model of culture dissemination, (iii) the garbage can model of organizational choice. The use of the concept of temporal networks allows us to show the dynamics of ideas spreading processes in networks, thanks to the analysis of contacts between agents in networks. The Axelrod culture dissemination model allows us to use the importance of cooperative behavior for the dynamics of ideas disseminated in networks. In the third model decisions on solutions of problems are made as an outcome of sequences of pseudorandom numbers. The origin of this model is the Herbert Simon’s view on bounded rationality. In the Axelrod model, ideas are conveyed by strings of symbols. The outcome of the model should be the diversity of evolving ideas as dependent on the chain length, on the number of possible values of symbols and on the threshold value of Hamming distance which enables the combination.

Garbage Can Model at Covid 19 Countermeasures Policy in Banten

The Garbage Can Model is a public policy model that is taken by the government when an urgent event occurs which requires a fast and populist policy. Covid 19 is a deadly virus that spreads very quickly and is transmitted through human intermediaries, data released by the Covid 19 Response Acceleration Task Force on April 24, 2020, at least 8,211 people tested positive for this virus, and those who died reached 689 people and recovered 1,002 people. As for cases in the world, 2,626,321 people have tested positive, who died reaching 181,938 people covering 213 countries / regions (WHO Data Last update: 24 April 2020, 07:00 GMT + 7). In tackling the spread of this virus, the state as the policy holder has a stake in protecting the safety of its citizens. However, there are several cases where the policy dynamics are very intense, resulting in policy actors not being able to fully utilize their rational capacities. This study focuses on a number of government policies in the response to covid 19 which especially occurs in Banten. This study uses a qualitative methodology with a descriptive approach to better describe how it is possible to understand all actors involved in the covid 19 prevention policy in Banten. Finding of this research garbage can model can’t runs well, the four element of garbage can such alternative choice (policy window), problems, solutions, and participants can’t go hand in hand. The very large role of the participants limits the roles of other elements that actually play an important role in shaping the garbage can.

How context and attention shape behaviors in online communities: a modified garbage can model

Online communities have emerged as important organizational forms, but there are many gaps in our understanding. In particular, researchers have mainly focused on individual-level drivers of behaviors in communities, while downplaying (formal, informal) context at various levels. We theorize that different dimensions of context (i.e. omnibus and discrete context) influence decision-making in online communities through mechanisms involving community members’ attention. Specifically, context influences which problems members perceive and which solutions they retrieve and apply, thereby shaping the process of matching solutions and problems. We derive four hypotheses about contribution behaviors in online communities and how such behaviors are influenced by context. The empirical setting for our study is the open-source software community. We find support for our hypotheses in a unique dataset that captures the behavior of 24,057 community members who used the SourceForge.net online platform from 2000 to 2002.

Decision-making in crisis management of a serious digital incident: A garbage can approach

Managing crisis challenges the ability to make numerous decisions under great uncertainty. This study address the decision-making process, and how the mix of involved individuals, prior knowledge, and available decision-makers forms the decisions made during a crisis. A large-scale exercise with a cyberattack scenario was chosen as the study’s case. The organization studied have highly skilled crisis management personnel; however, they are not used to manage a large-scale cyber-attack scenario. The garbage can model (GCM) of Organizational Choice with a few modifications is used as the analytical framework in the study.

Conflict, Chaos, and the Art of Institutional Design

The metaphor of the organization as a ``garbage can'' is intended as a playful insult. The Garbage Can Model concludes that ``organized anarchies,'' organizations characterized by unclear technology, problematic preferences, and fluid participation, make bad and unreliable decisions. However, management theorists in the 1970s and 1980s also saw a glimmer of hope through the Garbage Can Model's gloomy predictions. Moch and Pondy (1977), for example, propose that garbage can decision making could be robust to environmental ruggedness, ``the organizational equivalent of an all-terrain vehicle'' (360). In this paper, I explore the hypothesis that garbage cans can be adaptively rational organizational design. Using an agent-based computational model, I demonstrate how preference conflict and fluid participation in decision making promote effective search in uncertain task environments. I show that the political gridlock and unstable outcomes that emerge as a result of garbage can decision making -- the very features of garbage cans that make them perceived to be dysfunctional -- can facilitate short-term exploitation and long-term exploration of uncertain technical landscapes. In the medium-term, however, conflict stands in the way of the speedy ascent of local performance peaks, leading to degraded performance.

Surviving the reform: management usage of the garbage can model during implementation of reform

PurposeThis paper aims to apply the garbage can model to identify factors that affect managerial decision-making processes in educational systems undergoing reforms.Design/methodology/approachThis paper used a qualitative approach using semi-structured interviews with 39 teachers and managers in schools undergoing a system-wide reform.FindingsThe paper presents examples for a typology of decision outcomes found in the model and provides explanations for their emergence. It shows that there are many challenges that are associated with reform implementation and suggests factors that need to be taken into account when planning and implementing a reform.Originality/valueSchool management and policy makers can learn about the risks that are associated with garbage can decision-making and the various risk factors. Practical suggestions are given to reduce the probability of suboptimal decision-making.