Gaming the System: An Agent-Based Model of Estimation Strategies and Their Effects on System Performance

2017 ◽  
Author(s):  
John Meluso ◽  
Jesse Austin-Breneman
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
Model Simulation ◽  
System Level ◽  
Conservation Strategy ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Bias Estimation ◽  
Agent Based Model ◽  
Agent Based

Parameter estimates in large-scale complex engineered systems affect system evolution yet can be difficult and expensive to test. Systems engineering uses analytical methods to reduce uncertainty, but a growing body of work from other disciplines indicates that cognitive heuristics also affect decision-making. Interviews with several expert aerospace practitioners suggest that engineers bias estimation strategies. Practitioners reaffirmed known system features and posited that engineers may bias estimation methods as a negotiation and resource conservation strategy. Specifically, participants reported that some systems engineers “game the system” by biasing requirements to counteract subsystem estimation biases. An agent-based model simulation which recreates these characteristics is presented. Model results suggest that the systems engineers’ “gaming” strategy of counteracting subsystem bias may decrease system-level estimate accuracy and increase uncertainty.

Gaming the System: An Agent-Based Model of Estimation Strategies and their Effects on System Performance

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
John Meluso ◽  
Jesse Austin-Breneman
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
System Level ◽  
Conservation Strategy ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Bias Estimation ◽  
Agent Based Model ◽  
Agent Based ◽  
Complex Engineered Systems

Parameter estimates in large-scale complex engineered systems (LaCES) affect system evolution, yet can be difficult and expensive to test. Systems engineering uses analytical methods to reduce uncertainty, but a growing body of work from other disciplines indicates that cognitive heuristics also affect decision-making. Results from interviews with expert aerospace practitioners suggest that engineers bias estimation strategies. Practitioners reaffirmed known system features and posited that engineers may bias estimation methods as a negotiation and resource conservation strategy. Specifically, participants reported that some systems engineers “game the system” by biasing requirements to counteract subsystem estimation biases. An agent-based model (ABM) simulation which recreates these characteristics is presented. Model results suggest that system-level estimate accuracy and uncertainty depend on subsystem behavior and are not significantly affected by systems engineers' “gaming” strategy.

scholarly journals Social identity and meat consumption - An agent-based model

2021 ◽  
Author(s):  
Jiaqi Ge ◽  
Andrea Scalco ◽  
Tony Craig
Keyword(s):  
Social Identity ◽  
Social Interactions ◽  
Large Scale ◽  
Eating Behaviour ◽  
System Level ◽  
Dietary Behaviour ◽  
Agent Based Model ◽  
Agent Based ◽  
Meat Eating ◽  
Group Members

Humans are social animals. Even the very personal decision of what someone eats is influenced by others around them. In this study, we propose four social interaction mechanisms driven by social identity that affect a person’s decision to eat or not eat meat. Using data from the British Social Attitude Survey in 2014, we operationalise social identity in an agent-based model to study the effect of social interactions on the spread of meat-eating behaviour in the British population. We find that social interactions are crucial in determining the spread of meat-eating behaviour. In order to bring about large-scale behavioural changes at the system level, people need to 1) have a strong openness to influences from both in-group and out-group members who have a different dietary behaviour, and 2) have a weak tendency to reinforce their current behaviour after seeing in-group members sharing the same behaviour. The agent-based model is shown to be a useful tool to operationalise social theories in a well-defined context, and to upscale the system to study its dynamic evolutions under different scenarios.

Learning by failure vs learning by habits

2017 ◽  
Vol 23 (3) ◽  
pp. 524-546 ◽  
Author(s):  
Lorella Cannavacciuolo ◽  
Luca Iandoli ◽  
Cristina Ponsiglione ◽  
Giuseppe Zollo
Keyword(s):  
Learning Strategies ◽  
Diffusion Of Innovation ◽  
System Level ◽  
Alternative View ◽  
Agent Based Model ◽  
Content Type ◽  
Agent Based ◽  
Business Transactions ◽  
Virtual Lab ◽  
Start Up

Purpose The purpose of this paper is to explain the emergence of collaboration networks in entrepreneurial clusters as determined by the way entrepreneurs exchange knowledge and learn through business transactions needed to implement temporary supply chains in networks of co-located firms. Design/methodology/approach A socio-computational approach is adopted to model business transactions and supply chain formation in Marshallian industrial districts (IDs). An agent-based model is presented and used as a virtual lab to test the hypotheses between the firms’ behaviour and the emergence of structural properties at the system level. Findings The simulation findings and their validation based on the comparison with a real world cluster show that the topological properties of the emerging network are influenced by the learning strategies and decision-making criteria firms use when choosing partners. With reference to the specific case of Marshallian IDs it is shown that inertial learning based on history and past collaboration represents in the long term a major impediment for the emergence of hubs and of a network topology that is more conducive to innovation and growth. Research limitations/implications The paper offers an alternative view of entrepreneurial learning (EL) as opposed to the dominant view in which learning occurs as a result of exceptional circumstances (e.g. failure). The results presented in this work show that adaptive, situated, and day-by-day learning has a profound impact on the performance of entrepreneurial clusters. These results are encouraging to motivate additional research in areas such as in modelling learning or in the application of the proposed approach to the analysis of other types of entrepreneurial ecosystems, such as start-up networks and makers’ communities. Practical implications Agent-based model can support policymakers in identifying situated factors that can be leveraged to produce changes at the macro-level through the identification of suitable incentives and social networks re-engineering. Originality/value The paper presents a novel perspective on EL and offers evidence that micro-learning strategies adopted and developed in routine business transactions do have an impact on firms’ performances (survival and growth) as well as on systemic performances related to the creation and diffusion of innovation in firms networks.

scholarly journals Conceptual Agent Based Model Simulation for the Port Nautical Services

2019 ◽  
Vol 52 (3) ◽  
pp. 19-24
Author(s):  
I.Y. Davydenko ◽  
R.W. Fransen
Keyword(s):  
Model Simulation ◽  
Agent Based Model ◽  
Agent Based

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

scholarly journals An Agent Based Model to Detect Variations in the Transmission of Potters from Baetica Province

2021 ◽  
Author(s):  
Maria Coto-Sarmiento ◽  
Simon Carrignon
Keyword(s):  
Roman Empire ◽  
Social Learning ◽  
Material Culture ◽  
Learning Strategies ◽  
Large Scale ◽  
Scale Production ◽  
Agent Based Model ◽  
Pottery Production ◽  
Agent Based ◽  
Modes Of Transmission

The goal of this study is to analyse the transmission of technical skills among potters within the Roman Empire. Specifically, our case study has been focused on the production processes based on Baetica province (currently Andalusia) from 1st to 3rd century AD. Variability of material culture allows observing different production patterns that can explain how social learning evolves. Some differences can be detected in the making techniques processes through time and space that might explain different degrees of specialization. Unfortunately, it is extremely difficult to identify some evidence of social learning strategies in the archaeological record. In Archaeology, this process has been analysed by the study of the production of handmade pottery. In our case, we want to know if the modes of transmission could be similar with a more standardized production as Roman Age. We propose here an Agent-Based Model to compare different cultural processes of learning transmission. Archaeological evidence will be used to design the model. In this model, we implement a simple mechanism of pottery production with different social learning processes under different scenarios. In particular, the aim of this study is to quantify which one of those processes explain better the copying mechanisms among potters revealed in our dataset. We believe that the model presented here can provide a strong baseline for the exploration of transmission processes related to large-scale production.

scholarly journals Identifying Synergistic Interventions to Address COVID-19 Using a Large Scale Agent-Based Model

2020 ◽  
Author(s):  
Junjiang Li ◽  
Philippe J. Giabbanelli
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Contact Tracing ◽  
Remote Work ◽  
Agent Based Model ◽  
Agent Based ◽  
Global Pandemic ◽  
Small Set ◽  
Factorial Design Of Experiments ◽  
The Individual

AbstractThere is a range of public health tools and interventions to address the global pandemic of COVID-19. Although it is essential for public health efforts to comprehensively identify which interventions have the largest impact on preventing new cases, most of the modeling studies that support such decision-making efforts have only considered a very small set of interventions. In addition, previous studies predominantly considered interventions as independent or examined a single scenario in which every possible intervention was applied. Reality has been more nuanced, as a subset of all possible interventions may be in effect for a given time period, in a given place. In this paper, we use cloud-based simulations and a previously published Agent-Based Model of COVID-19 (Covasim) to measure the individual and interacting contribution of interventions on reducing new infections in the US over 6 months. Simulated interventions include face masks, working remotely, stay-at-home orders, testing, contact tracing, and quarantining. Through a factorial design of experiments, we find that mask wearing together with transitioning to remote work/schooling has the largest impact. Having sufficient capacity to immediately and effectively perform contact tracing has a smaller contribution, primarily via interacting effects.

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