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15 minutes ago
Kalyan Pradyumna Peddinti
Complex Systems and Advantages of Visual Decision support
COLLAPSE
Top of Form
Managing complex systems and the advantages of visual
decision support.
Agent-based modeling is commonly used in finding out the
various sorts of complex systems in different areas such as
science, sociology, and the environment. The role of
visualization of the execution, which is the complicated
frameworks, is that it helps with its ability to capture its
elements. In this case, the policy that is trying to create is the
use of renewable energy through vehicles within the city's smart
city. It is well known that petroleum products cause a lot of
pollution, and this will result in changing the city nit to be a
smart city again. Therefore, I would recommend the usage of
the renewable source of energy to propel the vehicles that will
emit the gas, which won’t be having a significant impact on the
people (Janssen, et al., 2015).
The main aim of the policy will be minimizing the cases of air
pollution, and through this, there will be climatic change.
Categorically, for practical application of agent-based modeling
through the experimental approach, it requires various
components. These components are, i) a well dynamic, crucial
and understandable visualization of its advancement under
different planning’s, ii) the ability to re-define this
visualization, in a perfect world in an intuitive manner, at
different spatial and temporal scales or using multiple
viewpoints, iii) the ability to discard abstract properties and
information from the components provided by the recreation and
to picture them into continuously iv) the capacity to interface
with such visualizations in a characteristics route so as to
change the model itself in an interactive design
approach (Janssen, et al., 2015).
About the various approaches to visualizing and find out data
elements like value, shading, direction, shape, and size. Some
of the ways of carrying out the named function of visualizing
and finding out of the data components are shading, book, 2D,
or 3D geometry. As indicated in figure 15.9, a means that
provided contextual investigations a short task portray; the
applied showing systems, the essential data types, the executed
perception methods, and the involved partner. Clearly, the table
shows the chosen case study varies in line with the stated
attributes. I this such case, it is essential to deduce that when
making policy, various procedures should be utilized
effectively. Data should be set to help in carrying the role of the
policymaking, and as earlier indicated, specific planning should
be set to assist the clients in the ideal way. There is an element
of heterogeneity of contextual analyses in the area of the policy
examination, as this helps in enhancing reality (Janssen, et
al., 2015). There is a need for the inclusion of the configuration
in the field of information representation and visual inspection
as of now deal with this particular issue.
References
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.).
(2015). Policy practice and digital science: Integrating complex
systems, social simulation and public administration in policy
research (Vol. 10). Springer.
Bottom of Form
45 minutes ago
Jithender Reddy Kallem
Week-12
COLLAPSE
Top of Form
Smartcity Evacuation using ABM-Based Gaming Simulation
The SmartCity evacuation policy involves devising policies that
are relevant in getting the involved stakeholders, towards a
prepared and disaster-ready city that can coordinate resources
and people during disaster more efficiently and seamlessly to
facilitate lower fatalities (Grignard & Drogoul, 2017). A
SmartCity Evacuation ABM-Based Gaming simulation for
policy making will integrate views and expertise from relevant
stakeholders including the public, who have largely grown to
question more about administrative functions offered to them.
The main stakeholders in the ABM-Based Evacuation policy
will include four main categories. The policy analysts are the
top tier lot who oversee the processing of the software data,
while domain experts include architects, disaster management
professionals and environmentalists. The lower tier stakeholders
will include policy makers who consist of the parliamentary
system while the citizens’ public participation of the process
falls last.
The simulation model constitutes of geospatial information,
building architectural designs and human population patterns
which are characteristic of a certain geographical area. The
model visualizes a series of information including population
patterns and geospatial distribution. Users can set up the
gaming simulation through specifying the stated parameters
including the type of disaster before running. The agents using
the model have a real time interaction with the simulated
environment and can see the effects on the environment and
geographical areas selected. The Dot map and heat map
visualization techniques will be used to view the effects of
evacuation policies that will be implemented by the relevant
stakeholders during such disasters in the SmartCity. Users can
hence analyze how human population would react to policies
meant to address evacuation issues during disasters in the
SmartCity (Janssen, Wimmer & Deljoo, 2015).
References
Grignard, A., & Drogoul, A. (2017, March). Agent-based
visualization: a real-time visualization tool applied both to data
and simulation outputs. In Workshops at the Thirty-First AAAI
Conference on Artificial Intelligence.
Janssen, M., Wimmer, M., & Deljoo, A. (2015). Policy practice
and Digital Science. Integrating Complex Systems, Social
Simulation and Public Administration in Policy Research.
Springer.
Bottom of Form
1 hour ago
Bharath Kumar Amma
Discussion week12
COLLAPSE
Top of Form
Smart city planning:
Using the information and communication technologies for the
policymaking process in smart city planning for effective land
usage, views of public transportation using the software
“URBAN FOOTPRINT.”
“Urban Footprint” smart city planning tool provides interactive
visualization; it is location Intelligence Software for Smart
Cities. This tool offers the best planning practices, with the
latest technologies for visualization and data sets, to analyses
the various factors while making policy on smart city planning.
Features are:
Building, planning choices with a simple to-utilize toolset of
building models. Comprehend future results with a suite of 12
analyzing modules
Entryway for Innovation with Extensive Data and Quick
Existing Conditions Analyses.
Building, planning options with a simple to-utilize toolset of
building and region models. Comprehend future results using
data sets analytic modules like emissions, vehicle-miles
traveled.
Engaging the city residents, community stakeholders,
Stakeholders involved are Policy analysts, who are the primary
users of the software who run it and produce the results, to
provide information, reports, etc. Domain experts like
architects, environmentalists, road and transport planning
authorities, and city planners — policymakers like the
politicians, beneficiaries. Residents of a city, since, public
participation is a critical factor in policymaking.
It has great data categories to get quicker insights, create
beautiful maps, analyzing the factors for planning. Like Land
Use, Transport & Circulation, Environment & Land Cover,
socio-Demographic, Map and measure dangerous and
atmosphere impacts with state-of-the-art sea rise, flood hazard,
and fire risk information from a large group of government,
state, and neighborhood sources, making regulatory and
summary reports.
Case Study: Smart City Planning. (effective land use,
infrastructure, transportation view)
Description
Smart City Planning
Modeling technique(s)
Urban Footprint software, analytics tool.
Data type
Data sets on land usage, transportation, wetlands, parks, Socio-
Demographic, sea rise, flood risk, and fire hazard data.
Visualizations
Visualize habitat areas, soils, water resource areas, agricultural
data, city maps, etc.
Involved stakeholders
City residents, politicians (policymakers), policy analysts,
expertise on smart city planning.
Reference:
https://medium.com/urbanfootprint -- Location Intelligence
Software for Smart Cities.
Bottom of Form
Week 11
1. Answer in 2-3 sentences.
Classify and describe the values attributed to data in a
computational model.
2. Answer in 2-3 sentences.
Classify and describe the values attributed to a computational
model.
3. Answer in 2-3 sentences.
Classify and describe the values attributed to the decision-
making process.
4. Write a reference.
Identify a recent scholarly article that presents possible
biases, arguments or values on a computational model and write
the reference to the article in APA format.
Week 12
1.Select two resources on visual decision support for policy-
making and write a short analysis (3-4 paragraphs). Include the
APA formatted references in your submission.
ITS 832
Chapter 15
Visual Decision Support for Policy Making:Advancing
Policy Analysis withVisualization
Information Technology in a Global Economy
Introduction
• Background
• Approach
• Case Studies
• Optimization
• Social Simulation
• Urban Planning
• Conclusion
Background
• Assessing policy options for societal problems is difficult
• Decision making methods
• Data driven
• Model driven
• Visual decision supports helps in evaluating model output
• Information visualization and visual analytics
• Makes complex results accessible to many
• Policy analysis
• Part of process aimed at solving societal problems
Data Visualization
Policy cycle
Approach
• Characterization of stakeholders
• Policy makers
• Policy analysts
• Modeling experts
• Domain experts
• Public stakeholders
• Bridging knowledge gaps
• With information visualization (IV)
• Cohesive view of model representation
Visual Support for Policy Analysis
Approach, cont’d.
• Synergy effects of applying IV to policy analysis
• Communication - facilitated
• Complexity - reduced
• Subjectivity - reduced
• Validation - improved
• Transparency and reproducibility of results - increased
Case Studies
• Optimization
• Optimization of regional energy plans considering impacts
• Environmental
• Economical
• Social
• Social Simulation
• Simulation of the impact of different policy instruments on the
adoption of photovoltaic (PV) panels by homeowners
• Urban planning
• Integration of heterogenous data sources in planning activities
Summary of Case Studies
Conclusion
• Current model output is often difficult to understand
• Not accessible for non-specialists
• Information visualization (IV)
• Makes model output more accessible
• This paper applies IV to policy analysis
• Contributions
• Defined collaborations
• Identified hurdles
• Defined interface methodology
ITS 832
CHAPTER 13
Management of Complex Systems:
Toward Agent-Based Gaming for Policy
Information Technology in a GlobalEconomy
INTRODUCTION
• Simulating/Managing Social Complex Phenomena
• Leadership and Management in ComplexSystems
• Serious Gaming
• Agent-Based Games for Testing Leadership and
Management
• Single and Multiplayer Settings
• Summary and conclusions
SIMULATING AND MANAGING SOCIAL
COMPLEX PHENOMENA
• Study of how people interact
• Scale prohibits experimentation with real populations
• Agent-Base modeling (ABM)
• Networked agents
• Each agent is an individual
• Interaction may modify agent behavior
• Managing complex phenomena introduces complexity
• Techniques to manage turbulent situations vary
• Technique success depends on responding to agent behavior
• Which may change based on interactions
LEADERSHIP AND MANAGEMENT IN
COMPLEX SYSTEMS
• Traditional leadership research
• Generally focuses on single period in time
• Doesn’t address dynamic relationships
• Timing of leadership principle application matters
• Primary leadership functions
• Instructional and regulatory
• Developmental
• Simulations offer promise to help model leadership in
complex
systems
SERIOUS GAMING
• Applying gaming techniques to real life situations
• Flight simulators
• Effective for evaluating complex environments
• Player must interact with multiple actors and situations
• Currently used for side range of training applications
• Leadership use
• Deterministic – limited scope
• ABMs in serious gaming can help understand more complex
interactions
AGENT-BASED GAMES FORTESTING
LEADERSHIP AND MANAGEMENT
• ABM games with autonomous AI population
• Test leadership style effectiveness
• Explore which styles work best in different situations
• Determine the best choice for a given scenario
• Current state of the art is more conceptual
• Advances needed in interfaces
• Need to allow users to interact with simulation
• Keep players engaged
BEHAVIOR IMPACTED BY MULTIPLE FACTORS
SINGLE AND MULTIPLAYER GAMES
• AI may react poorly to management input
• Simulating unexpected consequences of decisions
• Overactive AI may degrade realism
• Players can dynamically see how decisions affect others
• Early simulations allow for only single players
• Multiple real players adds more realistic interaction
• Players replace someAI
• Players interact with each other andAI
SUMMARY AND CONCLUSIONS
• ABM-based gaming can measure behaviors of players
• Supports experimentation in controlled environment
• Study leaderships and management in complex systems
• Focus
• Interaction with leadership
• Interaction with players as a result of leadership action
15 minutes agoKalyan Pradyumna Peddinti Complex Systems and .docx

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  • 1. 15 minutes ago Kalyan Pradyumna Peddinti Complex Systems and Advantages of Visual Decision support COLLAPSE Top of Form Managing complex systems and the advantages of visual decision support. Agent-based modeling is commonly used in finding out the various sorts of complex systems in different areas such as science, sociology, and the environment. The role of visualization of the execution, which is the complicated frameworks, is that it helps with its ability to capture its elements. In this case, the policy that is trying to create is the use of renewable energy through vehicles within the city's smart city. It is well known that petroleum products cause a lot of pollution, and this will result in changing the city nit to be a smart city again. Therefore, I would recommend the usage of the renewable source of energy to propel the vehicles that will emit the gas, which won’t be having a significant impact on the people (Janssen, et al., 2015). The main aim of the policy will be minimizing the cases of air pollution, and through this, there will be climatic change. Categorically, for practical application of agent-based modeling through the experimental approach, it requires various components. These components are, i) a well dynamic, crucial and understandable visualization of its advancement under different planning’s, ii) the ability to re-define this visualization, in a perfect world in an intuitive manner, at different spatial and temporal scales or using multiple viewpoints, iii) the ability to discard abstract properties and information from the components provided by the recreation and to picture them into continuously iv) the capacity to interface with such visualizations in a characteristics route so as to
  • 2. change the model itself in an interactive design approach (Janssen, et al., 2015). About the various approaches to visualizing and find out data elements like value, shading, direction, shape, and size. Some of the ways of carrying out the named function of visualizing and finding out of the data components are shading, book, 2D, or 3D geometry. As indicated in figure 15.9, a means that provided contextual investigations a short task portray; the applied showing systems, the essential data types, the executed perception methods, and the involved partner. Clearly, the table shows the chosen case study varies in line with the stated attributes. I this such case, it is essential to deduce that when making policy, various procedures should be utilized effectively. Data should be set to help in carrying the role of the policymaking, and as earlier indicated, specific planning should be set to assist the clients in the ideal way. There is an element of heterogeneity of contextual analyses in the area of the policy examination, as this helps in enhancing reality (Janssen, et al., 2015). There is a need for the inclusion of the configuration in the field of information representation and visual inspection as of now deal with this particular issue. References Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10). Springer. Bottom of Form 45 minutes ago Jithender Reddy Kallem Week-12 COLLAPSE
  • 3. Top of Form Smartcity Evacuation using ABM-Based Gaming Simulation The SmartCity evacuation policy involves devising policies that are relevant in getting the involved stakeholders, towards a prepared and disaster-ready city that can coordinate resources and people during disaster more efficiently and seamlessly to facilitate lower fatalities (Grignard & Drogoul, 2017). A SmartCity Evacuation ABM-Based Gaming simulation for policy making will integrate views and expertise from relevant stakeholders including the public, who have largely grown to question more about administrative functions offered to them. The main stakeholders in the ABM-Based Evacuation policy will include four main categories. The policy analysts are the top tier lot who oversee the processing of the software data, while domain experts include architects, disaster management professionals and environmentalists. The lower tier stakeholders will include policy makers who consist of the parliamentary system while the citizens’ public participation of the process falls last. The simulation model constitutes of geospatial information, building architectural designs and human population patterns which are characteristic of a certain geographical area. The model visualizes a series of information including population patterns and geospatial distribution. Users can set up the gaming simulation through specifying the stated parameters including the type of disaster before running. The agents using the model have a real time interaction with the simulated environment and can see the effects on the environment and geographical areas selected. The Dot map and heat map visualization techniques will be used to view the effects of evacuation policies that will be implemented by the relevant stakeholders during such disasters in the SmartCity. Users can hence analyze how human population would react to policies meant to address evacuation issues during disasters in the SmartCity (Janssen, Wimmer & Deljoo, 2015). References
  • 4. Grignard, A., & Drogoul, A. (2017, March). Agent-based visualization: a real-time visualization tool applied both to data and simulation outputs. In Workshops at the Thirty-First AAAI Conference on Artificial Intelligence. Janssen, M., Wimmer, M., & Deljoo, A. (2015). Policy practice and Digital Science. Integrating Complex Systems, Social Simulation and Public Administration in Policy Research. Springer. Bottom of Form 1 hour ago Bharath Kumar Amma Discussion week12 COLLAPSE Top of Form Smart city planning: Using the information and communication technologies for the policymaking process in smart city planning for effective land usage, views of public transportation using the software “URBAN FOOTPRINT.” “Urban Footprint” smart city planning tool provides interactive visualization; it is location Intelligence Software for Smart Cities. This tool offers the best planning practices, with the latest technologies for visualization and data sets, to analyses the various factors while making policy on smart city planning. Features are: Building, planning choices with a simple to-utilize toolset of building models. Comprehend future results with a suite of 12 analyzing modules Entryway for Innovation with Extensive Data and Quick Existing Conditions Analyses. Building, planning options with a simple to-utilize toolset of
  • 5. building and region models. Comprehend future results using data sets analytic modules like emissions, vehicle-miles traveled. Engaging the city residents, community stakeholders, Stakeholders involved are Policy analysts, who are the primary users of the software who run it and produce the results, to provide information, reports, etc. Domain experts like architects, environmentalists, road and transport planning authorities, and city planners — policymakers like the politicians, beneficiaries. Residents of a city, since, public participation is a critical factor in policymaking. It has great data categories to get quicker insights, create beautiful maps, analyzing the factors for planning. Like Land Use, Transport & Circulation, Environment & Land Cover, socio-Demographic, Map and measure dangerous and atmosphere impacts with state-of-the-art sea rise, flood hazard, and fire risk information from a large group of government, state, and neighborhood sources, making regulatory and summary reports. Case Study: Smart City Planning. (effective land use, infrastructure, transportation view) Description Smart City Planning Modeling technique(s) Urban Footprint software, analytics tool. Data type Data sets on land usage, transportation, wetlands, parks, Socio- Demographic, sea rise, flood risk, and fire hazard data. Visualizations Visualize habitat areas, soils, water resource areas, agricultural data, city maps, etc. Involved stakeholders City residents, politicians (policymakers), policy analysts, expertise on smart city planning.
  • 6. Reference: https://medium.com/urbanfootprint -- Location Intelligence Software for Smart Cities. Bottom of Form Week 11 1. Answer in 2-3 sentences. Classify and describe the values attributed to data in a computational model. 2. Answer in 2-3 sentences. Classify and describe the values attributed to a computational model. 3. Answer in 2-3 sentences. Classify and describe the values attributed to the decision- making process. 4. Write a reference. Identify a recent scholarly article that presents possible biases, arguments or values on a computational model and write the reference to the article in APA format. Week 12 1.Select two resources on visual decision support for policy- making and write a short analysis (3-4 paragraphs). Include the APA formatted references in your submission.
  • 7. ITS 832 Chapter 15 Visual Decision Support for Policy Making:Advancing Policy Analysis withVisualization Information Technology in a Global Economy Introduction • Background • Approach • Case Studies • Optimization • Social Simulation • Urban Planning • Conclusion Background • Assessing policy options for societal problems is difficult
  • 8. • Decision making methods • Data driven • Model driven • Visual decision supports helps in evaluating model output • Information visualization and visual analytics • Makes complex results accessible to many • Policy analysis • Part of process aimed at solving societal problems Data Visualization Policy cycle Approach • Characterization of stakeholders • Policy makers • Policy analysts • Modeling experts • Domain experts • Public stakeholders
  • 9. • Bridging knowledge gaps • With information visualization (IV) • Cohesive view of model representation Visual Support for Policy Analysis Approach, cont’d. • Synergy effects of applying IV to policy analysis • Communication - facilitated • Complexity - reduced • Subjectivity - reduced • Validation - improved • Transparency and reproducibility of results - increased Case Studies • Optimization • Optimization of regional energy plans considering impacts • Environmental • Economical • Social
  • 10. • Social Simulation • Simulation of the impact of different policy instruments on the adoption of photovoltaic (PV) panels by homeowners • Urban planning • Integration of heterogenous data sources in planning activities Summary of Case Studies Conclusion • Current model output is often difficult to understand • Not accessible for non-specialists • Information visualization (IV) • Makes model output more accessible • This paper applies IV to policy analysis • Contributions • Defined collaborations • Identified hurdles • Defined interface methodology ITS 832
  • 11. CHAPTER 13 Management of Complex Systems: Toward Agent-Based Gaming for Policy Information Technology in a GlobalEconomy INTRODUCTION • Simulating/Managing Social Complex Phenomena • Leadership and Management in ComplexSystems • Serious Gaming • Agent-Based Games for Testing Leadership and Management • Single and Multiplayer Settings • Summary and conclusions SIMULATING AND MANAGING SOCIAL COMPLEX PHENOMENA • Study of how people interact • Scale prohibits experimentation with real populations • Agent-Base modeling (ABM) • Networked agents • Each agent is an individual
  • 12. • Interaction may modify agent behavior • Managing complex phenomena introduces complexity • Techniques to manage turbulent situations vary • Technique success depends on responding to agent behavior • Which may change based on interactions LEADERSHIP AND MANAGEMENT IN COMPLEX SYSTEMS • Traditional leadership research • Generally focuses on single period in time • Doesn’t address dynamic relationships • Timing of leadership principle application matters • Primary leadership functions • Instructional and regulatory • Developmental • Simulations offer promise to help model leadership in complex systems SERIOUS GAMING
  • 13. • Applying gaming techniques to real life situations • Flight simulators • Effective for evaluating complex environments • Player must interact with multiple actors and situations • Currently used for side range of training applications • Leadership use • Deterministic – limited scope • ABMs in serious gaming can help understand more complex interactions AGENT-BASED GAMES FORTESTING LEADERSHIP AND MANAGEMENT • ABM games with autonomous AI population • Test leadership style effectiveness • Explore which styles work best in different situations • Determine the best choice for a given scenario • Current state of the art is more conceptual • Advances needed in interfaces • Need to allow users to interact with simulation • Keep players engaged
  • 14. BEHAVIOR IMPACTED BY MULTIPLE FACTORS SINGLE AND MULTIPLAYER GAMES • AI may react poorly to management input • Simulating unexpected consequences of decisions • Overactive AI may degrade realism • Players can dynamically see how decisions affect others • Early simulations allow for only single players • Multiple real players adds more realistic interaction • Players replace someAI • Players interact with each other andAI SUMMARY AND CONCLUSIONS • ABM-based gaming can measure behaviors of players • Supports experimentation in controlled environment • Study leaderships and management in complex systems • Focus • Interaction with leadership • Interaction with players as a result of leadership action