This document discusses simulations and virtual worlds in educational research. It covers the theoretical bases, applications, opportunities, and challenges of using simulations and virtual worlds. Some key points include:
- Simulations model and imitate real-world systems through mathematical relationships, allowing researchers to manipulate variables and observe outcomes. Virtual worlds are persistent online spaces created and shaped by user interactions.
- They allow for prediction, understanding, explanation, and safe exploration of concepts. Researchers have control and visibility while being economical. However, they are not a replacement for real-world experiences.
- Applications include modeling real-life scenarios, collecting large data sets, studying human interaction over time, and exploring sensitive issues. However, challenges include ensuring common
Tutorial sobre el buscador Google (operadores, trucos...) destinado a actividades de Alfabetización Informacional.
Tutorial on the Google search engine (operators, tricks ...) for Information Literacy activities.
Tutorial sobre el buscador Google (operadores, trucos...) destinado a actividades de Alfabetización Informacional.
Tutorial on the Google search engine (operators, tricks ...) for Information Literacy activities.
This is the presentation I gave at Gulltaggen as part of the "Three In One - Story, Technology and Team work" session: http://www.gulltaggen.no/2011/conference/day-1-12th-of-april/three-in-one-story-technology-and-team-work
Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)Giuseppe Vizzari
First lesson and introduction of the PhD course on "Computational approaches to Physical and Virtual Crowd Phenomena" - titled "Simulation of complex systems: the case of crowds"
This is the presentation I gave at Gulltaggen as part of the "Three In One - Story, Technology and Team work" session: http://www.gulltaggen.no/2011/conference/day-1-12th-of-april/three-in-one-story-technology-and-team-work
Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)Giuseppe Vizzari
First lesson and introduction of the PhD course on "Computational approaches to Physical and Virtual Crowd Phenomena" - titled "Simulation of complex systems: the case of crowds"
Approaches to gather business requirements, defining problem statements, business requirements for
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Simulation in Social Sciences - Lecture 6 in Introduction to Computational S...Lauri Eloranta
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ITS 832Chapter 4Policy Making and Modeling in aComplex.docxdonnajames55
ITS 832
Chapter 4
Policy Making and Modeling in a
Complex World
Information Technology in a Global Economy
Professor Miguel Buleje
Introduction
• Policy Making and Modeling in a ComplexWorld
• Complexity
• Managing Complex Systems
• Modelling for Complex Systems
Complexity
• System composed of multiple interacting elements
• Possible behavioral states can combine in ways that are hard to
predict
• Many complex systems in the physical world
• Adaptive capacity of organisms allow for long-term
survival in complex systems
• Complex Adaptive Systems (CAS)
• Strong capacity to self-organize
Double Pendulum Example
Common Mistakes in Managing
Complex Systems
• Quantification
• Policy is biased towards Quantifiable Variables.
• Most often, monetary quantification.
• Most often, solution selected would be favorable to the optimal economic
outcome.
• Commonly overlooks important non-quantifiable aspects
• Compartmentalization
• Second response by policymakers in trying to simplify complex system.
• Attempts to simplify complex social systems
• Large systems are split into smaller systems
• High RISK to miss interactions between smaller systems
• Spillover effect
Complexity in Policy Making
• Common approaches
• Instrumental
• Choosing between a set of possible policies
• Evaluated based on past effectiveness
• Requires
• Large enough pool of available strategies
• Effective assessment of effectiveness
• Representational
• Little more complication approach
• Series of models
• Each is assessed on its ability to predict observed behavior
Instrumental Approach
Representational Approach
Agent-based Simulation Models
• Agent-based simulation is presenting an optimal approach to address
issue around complexity and policymaking.
• Agent base models:
• Represents individuals as separate computer models
• Each model captures the behavior by each individual
• Agents interact through a network
• Distributed nature allows for realistic interactions, and makes this an
attractive alternative.
• SIMSOC
• Simulated Society: use by universities and other groups to teach
social sciences.
• Large modeling projects repository, per inventory on the SIMSOC,
in our space of policy making.
Summary
• Complex systems are difficult to model
• Interactions can be unpredictable
• Common mistakes in modeling complex systems
• Quantification
• Compartmentalization
• Two common approaches to complex system modeling
• Instrumental
• Representational
• Agent-based modeling
.
Lecture 11 of the COMP 4010 class on Augmented Reality and Virtual Reality. This lecture is about VR applications and was taught by Mark Billinghurst on October 19th 2021 at the University of South Australia
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Session Overview
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2. STRUCTURE OF THE CHAPTER
• Simulations and virtual worlds
• Theoretical bases of simulations and virtual
worlds
• Applications of virtual worlds
• A worked example of virtual world research
• Opportunities and limitations
• Issues and problems in virtual world research
• Using a virtual world and simulations in
educational research
• Ethical issues in virtual world research
• Online tools for data collection from virtual worlds
3. SIMULATIONS
Main components:
• A system of interrelated features in which the
researcher is interested and that lends itself
to be modelled or simulated.
• A model of that system that is often a
mathematical analogue.
• Deterministic simulations all the
mathematical relationships between the
components of a system are known.
• Stochastic simulations: at least one variable
is random.
4. COMPUTER SIMULATIONS ARE
CHARACTERIZED BY . . .
• Modelling and imitating the behaviour of
systems and their major attributes;
• Enabling researchers to see ‘what happens if’
the system is allowed to run its course or if
variables are manipulated;
• A mathematical formula that models key
features of the reality;
• Mathematical relationships that are assumed
to be repeating in controlled, bounded and
clearly defined situations, sometimes giving
rise to unanticipated outcomes.
5. COMPUTER SIMULATIONS ARE
CHARACTERIZED BY . . .
• Feedback and multiple iteration procedures
for understanding the emergence of
phenomena and behaviours;
• Complex phenomena and behaviours derived
from the repeated interplay of initial
conditions/variables;
• Deterministic laws (the repeated calculation
of a formula) sometimes leading to
unpredictable outcomes.
6. ‘WHAT IF’ QUESTIONS
• What happens if I change this parameter or
that parameter?
• What if the person behaves in such-and-such
a way?
• What happens if I change such-and-such a
feature of the environment?
7. ATTRACTIONS OF SIMULATIONS
• Prediction
• Understanding
• Explanation
• Exploration (in a safe environment)
• Virtual worlds are created by the participants
and the world emerges from the interaction of
the participants.
• Individuals can project their own views and
values on topics through their avatar and
receive the feedback of others in the system.
8. ATTRACTIONS OF SIMULATIONS
• Economy (they are cheaper to run than the real-
life situations);
• Visibility (they can make a phenomenon more
accessible and clear to the researcher);
• Control (the researcher has more control over
the simulation than in the real life situation);
• Safety (researchers can work on situations that
may be too dangerous, sensitive, ethically
questionable or difficult in real life natural
situations);
• Practice (they can be used for training).
9. RESERVATIONS ABOUT
SIMULATIONS
• Artificiality (they mimic life, rather than
being the real thing);
• Cost (computer simulations can be
expensive);
• Training of participants (simulations often
require considerable training);
• Quantitative problems (they may require
programming expertise).
10. Features and affordances of simulations/virtual worlds
Simulations Virtual worlds
Modeling / imitating Realizing / Acting
‘What if’ modeling of known
variables
Few known variables
An underlying mathematical
construct
Minimal underlying constructs
Modelled and interpreted
reality
Catching and manipulating the
fine grain of reality
Bounded, defined parameters Unbounded and undefined
parameters
Iteration and feedback to
reveal emergent phenomena
Human agency as the driver of
emergence
Repeated interplay of set initial
conditions
Any set initial conditions rapidly
abandoned
Unpredictable outcomes
sometimes
Unpredictable outcomes common
Limited simultaneous users Multiple simultaneous users
Transience Persistence
11. THEORETICAL BASES OF
SIMULATIONS AND VIRTUAL WORLDS
• Chaos theory
• Complexity theory
• Immersive experiences and co-presence
• Agent-based modelling
• Social facts
• Artificial life
• Social networking
• Communicative action (Habermas)
12. APPLICATIONS OF VIRTUAL WORLDS
• ‘Real life’ scenarios
• Collect large amount of data
• Store data
• Study of human interaction, especially in
dynamic, fluid, uncertain or contested
contexts
• Explore complex behaviour variables
• Monitor developments over time
• Explore sensitive issues
• Explore values and viewpoints
13. OPPORTUNITIES
• The experimenter has complete manipulative
control over every aspect of the situation (e.g. in
flight simulators, surgical simulators, training in
dangerous environments and decision-making
training).
• Participants are given a realistic situation in which
to act in whatever way they think appropriate.
• Inclusion of the time dimension allows the subject
to take an active role in interacting with the
environment, and enables the experimenter to
observe a social system in action, with feedback
loops and multidirectional causal connections.
14. OPPORTUNITIES
• Experiential and active learning;
• Encourages motivation and engagement;
• Visualization, managing complex environments;
• Access to impossible/difficult environments;
• Flexibility (can be programmed to offer wide
range of situations/stimuli);
• Monitoring (sessions can be recorded,
examined, evaluated and assessed);
• Connect geographically distant participants;
• Synchronous and asynchronous.
15. CHALLENGES
• Creating common research protocols
• Use of different IT systems
• Assumptions (and cultures) of participants
differ
• Necessary expertise and training in their use
(for researchers and participants)
• Sufficient bandwidth
• Ability to work with different firewalls
16. CONSIDERATIONS FOR
EDUCATIONAL RESEARCH
• Type of project
• Focus of the activity
• Carefully formulate the research question
• Venue: a private/closed or open environment
• Participants
• Methodology
• Ethical issues
• Data analysis
• Dissemination
17. ETHICAL ISSUES
• Vulnerability
• Individual risk
• Informed consent, especially when dealing
with:
– Online identities
– The nature of communication (public or
private)
– Security
– Confidentiality and privacy
– Inworld standards and rules