From KISS to TASS Modeling: A Preliminary Analysis of the Segregation Model Incorporated with Spatial Data on Chicago

2015 ◽  
Vol 16 (4) ◽  
pp. 553-573 ◽  
Author(s):  
GAKU ITO ◽  
SUSUMU YAMAKAGE
Keyword(s):  
Residential Segregation ◽  
Spatial Data ◽  
Demographic Data ◽  
Theoretical Models ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
The Us ◽  
Us Census ◽  
The City

AbstractThe ‘keep it simple, stupid’ slogan, or the KISS principle has been the basic guideline in agent-based modeling (ABM). While the KISS principle or parsimony is vital in modeling attempts, conventional agent-based models remain abstract and are rarely incorporated or validated with empirical data, leaving the links between theoretical models and empirical phenomena rather loose. This article reexamines the KISS principle and discusses the recent modeling attempts that incorporate and validate agent-based models with spatial (geo-referenced) data, moving beyond the KISS principle. This article also provides a working example of such time and space specified (TASS) agent-based models that incorporates Schelling's (1971) classic model of residential segregation with detailed geo-referenced demographic data on the city of Chicago derived from the US Census 2010.

scholarly journals Agent-Based Modeling and the City: A Gallery of Applications

2021 ◽  
pp. 885-910
Author(s):  
Andrew Crooks ◽  
Alison Heppenstall ◽  
Nick Malleson ◽  
Ed Manley
Keyword(s):  
Urban Growth ◽  
Agent Based Modeling ◽  
Machine Learning Techniques ◽  
Computational Power ◽  
Urban Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Learning Techniques ◽  
The City ◽  
Vast Range

AbstractAgent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

Abstract MP45: National Women’s Knowledge of Stroke Warning Signs, Overall and by Race/Ethnic Group

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Heidi Mochari-Greenberger ◽  
Amytis Towfighi ◽  
Lori Mosca
Keyword(s):  
Ethnic Group ◽  
Representative Sample ◽  
Demographic Data ◽  
Heart Association ◽  
Warning Signs ◽  
Warning Sign ◽  
The Us ◽  
English Speaking ◽  
Nationally Representative ◽  
Us Census

Background: Early treatment is associated with better clinical outcomes in stroke, but women must recognize the warning signs of a stroke to reduce delays in treatment. The purpose of this study was to evaluate contemporary knowledge of stroke warning signs and intent to call 9-1-1 first if warning signs occur, among a nationally representative sample of women, overall and by race/ethnic group. Methods: A study of cardiovascular disease awareness and knowledge was conducted by the American Heart Association in 2012 among English speaking US women > 25 years identified through random digit dialing (N=1,205; 54% white, 17% black, 17% Hispanic, 12% other). Demographic data, including race/ethnic group, were evaluated using standardized categorical questions. Knowledge about warning signs of stroke, and what to do first if experiencing signs of a stroke, was assessed by standardized unaided questions. Data were weighted to reflect the US population of women based on the US Census Bureau’s March 2011 Current Population Survey, overall and within ethnic strata. Results: In 2012, half of women surveyed (51%) identified sudden weakness/numbness of face/limb on one side as a stroke warning sign; this did not vary by race/ethnic group. Loss of/trouble talking/understanding speech was identified by 44% of women, and more frequently among white versus Hispanic women (48% vs. 36%; p<.05). Fewer than one in four women identified sudden severe headache (23%), unexplained dizziness (20%), or sudden dizziness/loss of vision (18%) as warning signs, and one in five (20%) did not know one stroke warning sign; these results did not vary by race/ethnicity. The majority of women said that they would call 9-1-1 first if they thought they were experiencing signs of a stroke (84%), and this did not vary among black (86%), Hispanic (79%), or white/other (85%) women. Conclusions: Knowledge of stroke warning signs was low among a nationally representative sample of women, especially among Hispanics. In contrast, knowledge to call 9-1-1 when experiencing signs of stroke was high. These data suggest effort to improve recognition of the warning signs of stroke has potential to reduce treatment delay and improve outcomes among women.

The Simulation Presented in the ODD Protocol

2017 ◽  
pp. 52-67
Keyword(s):  
Model Simulation ◽  
Behavioral Outcomes ◽  
Behavioral Model ◽  
Agent Based Modeling ◽  
State Variables ◽  
Agent Based Models ◽  
Agent Based ◽  
Design Concepts ◽  
Modeling Simulation ◽  
Area Of Interest

The ODD Protocol has become a standard for documenting and describing agent based models. The protocol is organized around three main elements, from which the ODD acronym originates: Overview, Design concepts, and Details. This chapter is organized around these primary elements and further broken down into seven sub-elements to provide a clear purpose and understanding of the model simulation. The sub-elements are: Purpose, State Variables and Scales, Process Overview and Scheduling, Design Concepts, Initialization, Input, and Sub-models. The model presented is a proto-agent behavioral model and is utilized in an agent based modeling simulation to help identify possible emergent behavioral outcomes of the populations in the area of interest. By varying the rules governing the interactions of the multinational and incumbent government proto-agents, different strategies can be identified for increasing the effectiveness of those proto-agents and the utilization of resources.

FDA analysis: Demographic trends in bladder and renal cancer trials.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18144-e18144
Author(s):  
Laura L Fernandes ◽  
Zhantao Lin ◽  
Lola A. Fashoyin-Aje ◽  
Shenghui Tang ◽  
Rajeshwari Sridhara ◽  
...  
Keyword(s):  
Renal Cancer ◽  
Demographic Data ◽  
Census Bureau ◽  
Incidence Rates ◽  
Test Results ◽  
Enrollment Trends ◽  
Black Patients ◽  
The Us ◽  
Chi Squared ◽  
Us Census

e18144 Background: Many publications report under representation of minorities in certain subgroups, which may limit the generalizability of clinical trial (CT) results. This analysis, investigates and reports enrollment trends in CTs submitted between 2006-2017 in support of marketing applications for drugs indicated for the treatment of urothelial (UC) and renal cancer carcinoma (RCC), and compares them to incidence rates of these diseases by Surveillance, Epidemiology, and End Results (SEER) registry and the US census bureau. Methods: We identified all marketing applications for the treatment of UC and RCC that provided the primary evidence of safety and efficacy and aggregated the demographic data across trials and disease. Using these two pooled datasets, we compared the patient proportions enrolled in each of the race, sex and age categories to the corresponding rates in US cancer population estimated based on the corresponding incidence rates reported by SEER and the US census bureau using a Chi-squared test. Results: The pooled seven UC and 14 RCC CTs provided 2035 and 6757 patients respectively. The results are summarized below for the 939 (46%) UC and 1489 (22%) RCC patients enrolled in the US. Conclusions: Our findings indicate that majority of the patients were enrolled outside of the US. There were lower proportion of Black patients (4% vs 8%), older patients, age ≥ 75 years (30% vs 48%) and males (74% vs 80%) enrolled in UC population in the US. Higher proportions were observed in both White (89% vs 85%) and Asian (4% vs 2%) patients in UC and in White (90% vs 79%) patients in RCC.[Table: see text]

COMPUTATIONAL FEATURES OF AGENT-BASED MODELS

2005 ◽  
Vol 02 (01) ◽  
pp. 33-48 ◽  
Author(s):  
MASSIMO BERNASCHI ◽  
FILIPPO CASTIGLIONE
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Agent Based Modeling ◽  
System Response ◽  
Agent Based Models ◽  
Design Data ◽  
Agent Based ◽  
Data Structures And Algorithms ◽  
Immune System Response ◽  
Large Scale Simulations

Agent-based modeling allows the description of very complex systems. To run large scale simulations of agent-based models in a reasonable time, it is crucial to carefully design data structures and algorithms. We describe the main computational features of agent-based models and report about the solutions we adopted in two applications: The simulation of the immune system response and the simulation of the stock market dynamics.

Using SysML for Conceptual Representation of Agent-Based Models

2011 ◽  
Author(s):  
Zhenghui Sha ◽  
Qize Le ◽  
Jitesh H. Panchal
Keyword(s):  
Conceptual Models ◽  
Agent Based Modeling ◽  
Modeling Language ◽  
Automatic Extraction ◽  
Systems Modeling ◽  
Conceptual Representation ◽  
Agent Based Models ◽  
Agent Based ◽  
Collaborative Processes ◽  
Limited Support

Agent-based modeling (ABM) is a technique used to simulate systems consisting of autonomous interacting entities called agents. It has shown great advantages in modeling complex systems with independent but interacting actors. ABM has been successfully applied to a variety of systems. Despite the availability of a large number of tools for ABM, there is limited support for the conceptual design of agent-based models. Further, the currently available tools capture both the model information and the tool-specific execution information in an integrated manner. This limits model reusability, which is an impediment to systematic validation of models. In this paper, we use the systems modeling language (SysML) for building conceptual models of agent-based models. We discuss how the different diagrams in the SysML language can be used to represent different aspects of agent-based models. Further, we propose an approach for automatically generating executable agent-based models from their conceptual SysML representations. The proposed approach is illustrated using a model of mass-collaborative processes as an example. The proposed approach for conceptual representation of agent-based models in SysML and automatic extraction of executable models has the potential to greatly improve reuse, reconfiguration, and validation of agent-based models.

Book Reviews

2017 ◽  
Vol 55 (2) ◽  
pp. 644-647
Keyword(s):  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Practical Guide ◽  
Interacting Agents ◽  
Hamilton College ◽  
Heterogeneous Interacting Agents

Christophre Georges of the Department of Economics, Hamilton College reviews “Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling,” edited by Alessandro Caiani, Alberto Russo, Antonio Palestrini, and Mauro Gallegati. The Econlit abstract for this book begins: “Text for graduate and PhD students, as well as undergraduates with some knowledge of computers and economics comprises four papers emerging from a workshop on agent-based modeling held by the Dipartimento di Scienze Economiche e Sociali at the Università Politecnica delle Marche. Presents a guide to agent-based models (ABM) and the technicalities that need to be solved in order to evaluate the effect of different rules and their switching.”

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