This document discusses concepts related to analyzing game and non-game systems. It introduces analysis frameworks like MDA and discusses analyzing systems using objects, properties, behaviors, and relationships. It also discusses analyzing real-world systems and emotional responses using MDA, using the example of analyzing a trip to the dentist. Additionally, it briefly outlines the life and philosophy of occultist Aleister Crowley and how his view of "living magically" through intentional acts can be applied to design.
This document discusses various tools and frameworks for ethical decision making, including decision analysis, IRAC analysis, the Five Whys technique, the DMAIC framework, the Seven Quality Tools, rational choice theory, image theory, and decision mapping. It provides an overview and brief description of each tool or framework and how they can be applied to evaluate situations and make choices consistent with ethical principles while considering consequences and values. The goal is to understand different approaches for structuring complex decisions and determining the best choice.
1.[1 8]an affective decision making engine framework for practical software a...Alexander Decker
The document describes a framework for an Affective Decision Making Engine that aims to emulate psychological affect in software agents to improve decision making in complex, dynamic environments. The engine measures correlations between environmental features and the agent's goal values to determine which goals may be positively or negatively impacted. Goals with high positive or negative correlations are assigned affect values, influencing the agent's action selection to maintain goal achievement. By basing affect on objective correlation data rather than a cognitive model, the engine can adaptively prioritize goals in response to environmental changes without requiring pre-defined contexts.
Using Modelling and Simulation for Policy Decision Support in Identity Manage...gueste4e93e3
The process of making IT (security) policy decisions, within organizations, is complex: it involves reaching consensus between a set of stakeholders (key decision makers, e.g. CISOs/CIOs, domain experts, etc.) who might have different views, opinions and biased perceptions of how policies need to be shaped. This involves multiple negotiations and interactions between stakeholders. This suggests two roles for policy decision support tools and methods: firstly to help an individual stakeholder test and refine their understanding of the situation and, secondly, to support the formation of consensus by helping stakeholders to share their assumptions and conclusions. We argue that an approach based on modeling and simulation can help with both these aspects, moreover we show that it is possible to integrate the assumptions made so that they can be directly contrasted and discussed. We consider, as a significant example, an Identity and Access Management (IAM) scenario: we focus on the provisioning process of user accounts on enterprise applications and services, a key IAM feature that has an impact on security, compliance and business outcomes. Whilst security and compliance experts might worry that ineffective policies for provisioning could fuel security and legal threats, business experts might be against policies that dictate overly strong or bureaucratic processes as they could have a negative impact on productivity. We explore the associated policy decision making process from these different perspectives and show how our systems modeling approach can provide consistent or comparable data, explanations, “what-if” predictions and analysis at different levels of abstractions. We discuss the implications that this has on the actual IT (security) policy decision making process.
This document provides an introduction to system dynamics concepts. It discusses that systems are complex and interconnected, and interventions in one part can have unintended consequences in other parts. System dynamics uses feedback loops and computer simulations to model complex social and technical systems over time. Mental models, delays, and non-linear feedbacks can all contribute to unexpected and counterintuitive system behaviors. System dynamics is an interdisciplinary approach that can help address complex problems by taking a holistic, long-term view of systems and policies.
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...Dr. Elliot Bendoly
This document summarizes an experiment on problem complexity, group synergy and performance. It found:
1) Nominal groups generally outperformed collaborative groups on more complex tasks, as complexity makes group benefits less clear.
2) Groups of specialists performed significantly better than generalist groups in both nominal and collaborative settings, especially on complex tasks.
3) In the first problem-solving period, intelligent search coverage of the solution space, rather than number of solutions, best predicted performance for individuals and specialists. Production blocking hindered collaborative generalist groups.
The document discusses problem situations and outlines several techniques for understanding and representing them, including mind maps, rich pictures, and cognitive mapping. It defines a problem as a situation where a decision maker is dissatisfied with the current state and has goals they want to achieve. Understanding the problem context involves identifying stakeholders and their different roles. Mind maps and rich pictures are diagramming methods that can help capture the complexity of a problem situation, including both tangible and intangible aspects. Guidelines are provided for constructing mind maps and rich pictures to represent elements, processes, and their relationships. Their main use is in communicating understanding of complex problems to others.
This document provides an overview of key concepts in modeling and simulation for decision support. It defines complex systems, open and closed systems, and hierarchical systems. It describes the differences between hard and soft problems, and the characteristics of hard systems and soft systems approaches. It also defines static and dynamic systems, and different types of models. Finally, it discusses the relationship between modeling and simulation and the key steps in a simulation process.
1. The study examined the neural correlates of encoding and integrating belief information during moral judgment.
2. The right temporo-parietal junction (RTPJ) was found to be important for both encoding and integrating beliefs.
3. The dorsomedial prefrontal cortex (dMPFC) was involved in processing belief valence, particularly for negative beliefs, during the integration phase.
This document discusses various tools and frameworks for ethical decision making, including decision analysis, IRAC analysis, the Five Whys technique, the DMAIC framework, the Seven Quality Tools, rational choice theory, image theory, and decision mapping. It provides an overview and brief description of each tool or framework and how they can be applied to evaluate situations and make choices consistent with ethical principles while considering consequences and values. The goal is to understand different approaches for structuring complex decisions and determining the best choice.
1.[1 8]an affective decision making engine framework for practical software a...Alexander Decker
The document describes a framework for an Affective Decision Making Engine that aims to emulate psychological affect in software agents to improve decision making in complex, dynamic environments. The engine measures correlations between environmental features and the agent's goal values to determine which goals may be positively or negatively impacted. Goals with high positive or negative correlations are assigned affect values, influencing the agent's action selection to maintain goal achievement. By basing affect on objective correlation data rather than a cognitive model, the engine can adaptively prioritize goals in response to environmental changes without requiring pre-defined contexts.
Using Modelling and Simulation for Policy Decision Support in Identity Manage...gueste4e93e3
The process of making IT (security) policy decisions, within organizations, is complex: it involves reaching consensus between a set of stakeholders (key decision makers, e.g. CISOs/CIOs, domain experts, etc.) who might have different views, opinions and biased perceptions of how policies need to be shaped. This involves multiple negotiations and interactions between stakeholders. This suggests two roles for policy decision support tools and methods: firstly to help an individual stakeholder test and refine their understanding of the situation and, secondly, to support the formation of consensus by helping stakeholders to share their assumptions and conclusions. We argue that an approach based on modeling and simulation can help with both these aspects, moreover we show that it is possible to integrate the assumptions made so that they can be directly contrasted and discussed. We consider, as a significant example, an Identity and Access Management (IAM) scenario: we focus on the provisioning process of user accounts on enterprise applications and services, a key IAM feature that has an impact on security, compliance and business outcomes. Whilst security and compliance experts might worry that ineffective policies for provisioning could fuel security and legal threats, business experts might be against policies that dictate overly strong or bureaucratic processes as they could have a negative impact on productivity. We explore the associated policy decision making process from these different perspectives and show how our systems modeling approach can provide consistent or comparable data, explanations, “what-if” predictions and analysis at different levels of abstractions. We discuss the implications that this has on the actual IT (security) policy decision making process.
This document provides an introduction to system dynamics concepts. It discusses that systems are complex and interconnected, and interventions in one part can have unintended consequences in other parts. System dynamics uses feedback loops and computer simulations to model complex social and technical systems over time. Mental models, delays, and non-linear feedbacks can all contribute to unexpected and counterintuitive system behaviors. System dynamics is an interdisciplinary approach that can help address complex problems by taking a holistic, long-term view of systems and policies.
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...Dr. Elliot Bendoly
This document summarizes an experiment on problem complexity, group synergy and performance. It found:
1) Nominal groups generally outperformed collaborative groups on more complex tasks, as complexity makes group benefits less clear.
2) Groups of specialists performed significantly better than generalist groups in both nominal and collaborative settings, especially on complex tasks.
3) In the first problem-solving period, intelligent search coverage of the solution space, rather than number of solutions, best predicted performance for individuals and specialists. Production blocking hindered collaborative generalist groups.
The document discusses problem situations and outlines several techniques for understanding and representing them, including mind maps, rich pictures, and cognitive mapping. It defines a problem as a situation where a decision maker is dissatisfied with the current state and has goals they want to achieve. Understanding the problem context involves identifying stakeholders and their different roles. Mind maps and rich pictures are diagramming methods that can help capture the complexity of a problem situation, including both tangible and intangible aspects. Guidelines are provided for constructing mind maps and rich pictures to represent elements, processes, and their relationships. Their main use is in communicating understanding of complex problems to others.
This document provides an overview of key concepts in modeling and simulation for decision support. It defines complex systems, open and closed systems, and hierarchical systems. It describes the differences between hard and soft problems, and the characteristics of hard systems and soft systems approaches. It also defines static and dynamic systems, and different types of models. Finally, it discusses the relationship between modeling and simulation and the key steps in a simulation process.
1. The study examined the neural correlates of encoding and integrating belief information during moral judgment.
2. The right temporo-parietal junction (RTPJ) was found to be important for both encoding and integrating beliefs.
3. The dorsomedial prefrontal cortex (dMPFC) was involved in processing belief valence, particularly for negative beliefs, during the integration phase.
The document discusses systems thinking and various systems thinking concepts and tools. It defines systems thinking as examining how problems are created and seeing the big picture by understanding how structure influences system performance. It discusses key systems thinking concepts like complex adaptive systems, feedback loops, stocks and flows. It also outlines different systems thinking tools like causal loop diagrams, stock and flow maps, behavior over time graphs and system archetypes that can help understand complex systems.
This seminar report provides an overview of systems thinking and key concepts. It defines systems thinking as viewing problems as parts of an overall system rather than in isolation. A system is a collection of parts integrated to accomplish an overall goal, with inputs, processes, outputs and outcomes, and feedback. Systems can be biological, mechanical, social, or other types and range from simple to complex. Systems theory studies principles that can be applied to all types of systems. Some basic principles of systems thinking discussed are that change is slow but lasting, cause and effect are not always closely linked, and easy answers often do not address complexity. The report also lists examples of systems principles like how a system's behavior depends on its structure and how systems seek
This document discusses systems and their characteristics. Some key points:
- A system is a group of interacting components that form a unified whole. Components can be physical or intangible.
- For something to be a system, all its parts must be present and arranged in a specific way to serve the system's purpose.
- Systems have specific purposes within larger systems and maintain stability through feedback and adjustments.
- Understanding a system's structure provides insight into patterns of events within that system over time. Activities are suggested to analyze systems and patterns.
The document provides an overview of system analysis and design. It discusses the system development life cycle which includes recognition of needs, feasibility study, analysis, design, implementation, and maintenance. It also covers the role of the systems analyst and various techniques used in systems analysis such as information gathering, structured analysis tools, and feasibility analysis. The goal of system analysis is to thoroughly understand the existing system and determine how computers can be used to make the system more effective.
Do we really need game testers in development teams? What is it that defines the core competence of a tester, and does this competence add any value to the development team?
Introduction to Systems Thinking by Daniel H. KimCláudio Siervi
This document introduces systems thinking and defines what a system is. It explains that a system is a group of interacting, interrelated, or interdependent parts that form a complex whole with a specific purpose. All systems maintain stability through feedback. Understanding a system's purpose is key, though natural and social systems can be more difficult to understand since their purposes may evolve. Systems thinking provides a perspective for seeing how systems fit into a broader context, including looking at events, patterns, and systemic structures. The document outlines some defining characteristics of systems and provides examples to illustrate these concepts.
The document introduces use case diagrams, which provide a high-level overview of interactions with a system from the perspective of actors. It describes the key elements of use case diagrams including actors, the system, use cases, and relationships. It provides examples of simple use case diagrams and explains how they can be expanded to show more detail or different views. The document also discusses how to identify and document use cases through event decomposition and analysis of external, temporal, and state-based events.
Only TeamQuest Combines 15+ years of Proven Success in Recruitment, Team Building & Coaching with an Award-Winning, Bias-Free, Innovation called Teamability® tech.
What Sets TeamQuest Advisors Apart?
We believe business is a Team Sport; we walk the talk by applying Teamability tech. Teamability measures the # 1 Performance Indicator: How People Will Perform in Teams.
This incredible new tech, at the centre of our approach, uncovers root causes to problems and provides highly relevant information; enabling effective strategies and actions that dramatically improve individual, team and company performance!
Business results depend on how people ‘team’ with each other that’s why TeamQuest solutions are based on ‘team performance metrics’.
Evolution as a Tool for Understanding and Designing Collaborative SystemsWilfried Elmenreich
The document discusses using evolutionary algorithms and modeling to understand cooperative behavior in complex systems. It describes how agent behaviors can be evolved using a genetic algorithm to optimize behaviors according to a fitness function. Simulation results show how cooperation can evolve over time in models of problems like the prisoner's dilemma and public goods games under different selection pressures and environment structures. Challenges in the evolutionary modeling approach are also discussed.
The document discusses systems thinking and its application to project management. It defines a system as interconnected parts that work together toward a common goal. A project is described as a system of people, equipment, and facilities organized to achieve an objective. Effective project management requires seeing how a project relates to and integrates with the overall organization as a system. Key elements of systems and how they apply to conceptualizing and managing projects are outlined.
Applying Systems Thinking to Solve Wicked Problems in Software EngineeringMajed Ayyad
Software systems are essentially socio-technical systems
and they are not isolated from other systems engineering processes. Unconsciously or by intention, we implement systems thinking in multi-agent systems, microservices, DevOps, distributed systems, API-led integrations and lean based software development life cycles. However, the concrete relationship between systems thinking and software engineering is still a green area and barely highlighted as a common practice among software engineers. In this presentation, we will
elaborate how systems thinking helps us to understand the socio-technical aspects of software engineering. We will discuss why systems thinking is important in the field of software engineering, provide examples where it is currently used and show the general areas where systems thinking applies to tackle complex software problems
The system analyst is responsible for developing software and hardware solutions to make an organization run efficiently. An analyst must have both interpersonal and technical skills, including communication, understanding technical concepts, teaching others, selling ideas, creativity, problem-solving, and project management. They are also expected to have experience in areas like systems analysis, programming languages, and hardware and software specifications. As a change agent, investigator, architect, psychologist, salesperson, motivator, and politician, the analyst plays a key role in developing, implementing, and ensuring adoption of new systems.
On Analyzing Self-Driving Networks: A Systems Thinking Approach Junaid Qadir
This document provides an overview of systems thinking approaches for analyzing self-driving networks. It discusses the problems with conventional non-systems thinking, such as mental models and reductionism. It then defines key concepts in systems thinking like feedback loops, leverage points, and archetypes. The document applies these concepts to challenges in internet architecture like spam, privacy, and quality of service. It also discusses ethical and policy challenges for self-driving networks, like who will make ethical decisions. The document concludes that systems thinking is needed to understand complex interactions in self-driving networks and their effects on stakeholders.
The system analyst is responsible for developing software and hardware solutions to make an organization run efficiently. An analyst must have strong interpersonal and technical skills to understand an organization's goals and needs, and to design systems that meet those needs. Key skills for an analyst include communication, understanding users, teaching others how to use new systems, selling ideas, problem-solving, project management, and having both technical knowledge and an understanding of business functions. Analysts play many roles such as a change agent to introduce new systems, an investigator to understand current problems, an architect to design technical solutions, a psychologist to understand users, a salesperson to convince users of new systems, a motivator to encourage users, and a politician to manage relationships
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
The document discusses object-oriented analysis and design (OOAD). It describes OOAD as an approach that models a system using interacting objects. It covers key concepts like use case modeling, identifying actors and use cases, domain modeling to define classes and relationships. It also compares the traditional and object-oriented approaches to software development.
The document discusses human motivation and incentives for contributing to Web 2.0 platforms. It covers theories of motivation like need theories, job characteristics approach, and reinforcement theory. It also discusses intrinsic and extrinsic motivations as well as designing incentive systems using game theory and mechanism design. A case study is presented on designing incentives for semantic annotation at a research organization through workshops, interviews, and a lab experiment comparing incentive systems.
AI is the study and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. Key applications of AI include advanced web search, recommendation systems, speech recognition in digital assistants, self-driving cars, and game playing. The goal of AI is to create systems that can think and act rationally. While progress has been made, fully simulating human intelligence remains a challenge.
The document discusses systems thinking and various systems thinking concepts and tools. It defines systems thinking as examining how problems are created and seeing the big picture by understanding how structure influences system performance. It discusses key systems thinking concepts like complex adaptive systems, feedback loops, stocks and flows. It also outlines different systems thinking tools like causal loop diagrams, stock and flow maps, behavior over time graphs and system archetypes that can help understand complex systems.
This seminar report provides an overview of systems thinking and key concepts. It defines systems thinking as viewing problems as parts of an overall system rather than in isolation. A system is a collection of parts integrated to accomplish an overall goal, with inputs, processes, outputs and outcomes, and feedback. Systems can be biological, mechanical, social, or other types and range from simple to complex. Systems theory studies principles that can be applied to all types of systems. Some basic principles of systems thinking discussed are that change is slow but lasting, cause and effect are not always closely linked, and easy answers often do not address complexity. The report also lists examples of systems principles like how a system's behavior depends on its structure and how systems seek
This document discusses systems and their characteristics. Some key points:
- A system is a group of interacting components that form a unified whole. Components can be physical or intangible.
- For something to be a system, all its parts must be present and arranged in a specific way to serve the system's purpose.
- Systems have specific purposes within larger systems and maintain stability through feedback and adjustments.
- Understanding a system's structure provides insight into patterns of events within that system over time. Activities are suggested to analyze systems and patterns.
The document provides an overview of system analysis and design. It discusses the system development life cycle which includes recognition of needs, feasibility study, analysis, design, implementation, and maintenance. It also covers the role of the systems analyst and various techniques used in systems analysis such as information gathering, structured analysis tools, and feasibility analysis. The goal of system analysis is to thoroughly understand the existing system and determine how computers can be used to make the system more effective.
Do we really need game testers in development teams? What is it that defines the core competence of a tester, and does this competence add any value to the development team?
Introduction to Systems Thinking by Daniel H. KimCláudio Siervi
This document introduces systems thinking and defines what a system is. It explains that a system is a group of interacting, interrelated, or interdependent parts that form a complex whole with a specific purpose. All systems maintain stability through feedback. Understanding a system's purpose is key, though natural and social systems can be more difficult to understand since their purposes may evolve. Systems thinking provides a perspective for seeing how systems fit into a broader context, including looking at events, patterns, and systemic structures. The document outlines some defining characteristics of systems and provides examples to illustrate these concepts.
The document introduces use case diagrams, which provide a high-level overview of interactions with a system from the perspective of actors. It describes the key elements of use case diagrams including actors, the system, use cases, and relationships. It provides examples of simple use case diagrams and explains how they can be expanded to show more detail or different views. The document also discusses how to identify and document use cases through event decomposition and analysis of external, temporal, and state-based events.
Only TeamQuest Combines 15+ years of Proven Success in Recruitment, Team Building & Coaching with an Award-Winning, Bias-Free, Innovation called Teamability® tech.
What Sets TeamQuest Advisors Apart?
We believe business is a Team Sport; we walk the talk by applying Teamability tech. Teamability measures the # 1 Performance Indicator: How People Will Perform in Teams.
This incredible new tech, at the centre of our approach, uncovers root causes to problems and provides highly relevant information; enabling effective strategies and actions that dramatically improve individual, team and company performance!
Business results depend on how people ‘team’ with each other that’s why TeamQuest solutions are based on ‘team performance metrics’.
Evolution as a Tool for Understanding and Designing Collaborative SystemsWilfried Elmenreich
The document discusses using evolutionary algorithms and modeling to understand cooperative behavior in complex systems. It describes how agent behaviors can be evolved using a genetic algorithm to optimize behaviors according to a fitness function. Simulation results show how cooperation can evolve over time in models of problems like the prisoner's dilemma and public goods games under different selection pressures and environment structures. Challenges in the evolutionary modeling approach are also discussed.
The document discusses systems thinking and its application to project management. It defines a system as interconnected parts that work together toward a common goal. A project is described as a system of people, equipment, and facilities organized to achieve an objective. Effective project management requires seeing how a project relates to and integrates with the overall organization as a system. Key elements of systems and how they apply to conceptualizing and managing projects are outlined.
Applying Systems Thinking to Solve Wicked Problems in Software EngineeringMajed Ayyad
Software systems are essentially socio-technical systems
and they are not isolated from other systems engineering processes. Unconsciously or by intention, we implement systems thinking in multi-agent systems, microservices, DevOps, distributed systems, API-led integrations and lean based software development life cycles. However, the concrete relationship between systems thinking and software engineering is still a green area and barely highlighted as a common practice among software engineers. In this presentation, we will
elaborate how systems thinking helps us to understand the socio-technical aspects of software engineering. We will discuss why systems thinking is important in the field of software engineering, provide examples where it is currently used and show the general areas where systems thinking applies to tackle complex software problems
The system analyst is responsible for developing software and hardware solutions to make an organization run efficiently. An analyst must have both interpersonal and technical skills, including communication, understanding technical concepts, teaching others, selling ideas, creativity, problem-solving, and project management. They are also expected to have experience in areas like systems analysis, programming languages, and hardware and software specifications. As a change agent, investigator, architect, psychologist, salesperson, motivator, and politician, the analyst plays a key role in developing, implementing, and ensuring adoption of new systems.
On Analyzing Self-Driving Networks: A Systems Thinking Approach Junaid Qadir
This document provides an overview of systems thinking approaches for analyzing self-driving networks. It discusses the problems with conventional non-systems thinking, such as mental models and reductionism. It then defines key concepts in systems thinking like feedback loops, leverage points, and archetypes. The document applies these concepts to challenges in internet architecture like spam, privacy, and quality of service. It also discusses ethical and policy challenges for self-driving networks, like who will make ethical decisions. The document concludes that systems thinking is needed to understand complex interactions in self-driving networks and their effects on stakeholders.
The system analyst is responsible for developing software and hardware solutions to make an organization run efficiently. An analyst must have strong interpersonal and technical skills to understand an organization's goals and needs, and to design systems that meet those needs. Key skills for an analyst include communication, understanding users, teaching others how to use new systems, selling ideas, problem-solving, project management, and having both technical knowledge and an understanding of business functions. Analysts play many roles such as a change agent to introduce new systems, an investigator to understand current problems, an architect to design technical solutions, a psychologist to understand users, a salesperson to convince users of new systems, a motivator to encourage users, and a politician to manage relationships
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
The document discusses object-oriented analysis and design (OOAD). It describes OOAD as an approach that models a system using interacting objects. It covers key concepts like use case modeling, identifying actors and use cases, domain modeling to define classes and relationships. It also compares the traditional and object-oriented approaches to software development.
The document discusses human motivation and incentives for contributing to Web 2.0 platforms. It covers theories of motivation like need theories, job characteristics approach, and reinforcement theory. It also discusses intrinsic and extrinsic motivations as well as designing incentive systems using game theory and mechanism design. A case study is presented on designing incentives for semantic annotation at a research organization through workshops, interviews, and a lab experiment comparing incentive systems.
AI is the study and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. Key applications of AI include advanced web search, recommendation systems, speech recognition in digital assistants, self-driving cars, and game playing. The goal of AI is to create systems that can think and act rationally. While progress has been made, fully simulating human intelligence remains a challenge.
2. Advanced Analysis
Throughout the program you have been looking
at analysis.
Deconstructing games, looking at systems,
working out mechanics.
3. Advanced Analysis
But this has always been an isolated action:
Something done in a single course.
Something that is described but the process has
been left up to you
4. Advanced Analysis
This course is based on analysing, and isolating
systems.
So understanding how to do this, we’re going to
go through different processes
5. Analysis
Analysis is the process
of transforming a
problem definition from a
fuzzy set of facts and
myths into a coherent
statement of a system’s
requirements.
6. Analysis
The main objective of the
analysis is to capture:
– a complete,
unambiguous, and
consistent picture of the
requirements of the
system and
– what the system must
do to satisfy the users'
requirements and needs.
7. Systems
Let’s start with a definition:
A system is a set of interrelated components that
function together to achieve a common goal. The
components of a system are called subsystems.
The components of a system are interdependent;
that is, the output of one subsystem is usually
becomes the input of another subsystem. Thus,
malfunctioning of one component affects the
functioning of other components.
8. Systems
A system receives inputs from the outside
environment, which are then processed by various
subsystems, and then delivers required outputs to
the outside environment.
A system also has control mechanisms to make
certain decisions. This is usually performed as a
feedback to the system user (or automated to the
system environment) followed by certain decisions.
9. Use Cases
When looking at the system as a whole, Use Case
Analysis identifies all the major uses of
the system. It is a functional description of the
entire system.
10. Use Cases
· Use Cases are the main tasks performed by the
users of the system.
· Use Cases describe the behavioural aspects of
the system.
· Use Cases are used to identify how the system
will be used.
· Use Cases are a convenient way to document
the functions that the system must support.
· Use Cases are used to identify the components
(objects) of the system.
11. Use Cases
Describe each use case, actor and relationship.
Describe how the use case interacts with
the actor as opposed to how it will perform its task.
12. Use Cases
Use Case examples
• Clerk prints a sales receipt for a video rental.
• Person spell checks a typed document.
• Receptionist schedules an appointment.
• Advisor registers student for classes.
14. Physical Model
To create a physical model, the following questions
are asked:
• Who performs the tasks?
• How they are performed?
• When or how often they are performed?
• How the data is stored (media)?
• How the dataflows are implemented
(media)?
15. Objects, Properties, Behaviours,
Relationships
This brings us back to the familiar ground of
analysis that you are all aware of. The “Game
Design Workbook” describes systems. It says
The basic elements of systems are objects,
properties, behaviours, and relationships. Objects
within the system interact with each other according
to their properties, behaviours, and relationships,
causing changes to the system state. How those
changes are manifested depend on the nature of
the objects and interactions.
16. Objects
Objects are the basic building blocks of a system.
Systems can be thought of as a group of
interrelated pieces called objects, which may be
physical, abstract, or both, depending on the nature
of the system.
Examples of objects in games might be individual
game pieces (such as the “king” or “ queen ” in
chess), in-game concepts (such as the “bank” in
Monopoly ), the players themselves , or
representations of the players (such as the avatars
in an online environment)
17. Objects
Objects are defined by their properties and
behaviors. They are also defined by their
relationships with other objects.
18. Properties
Properties are qualities or attributes that define
physical or conceptual aspects of objects.
Generally, these are a set of values that describe
an object.
19. Properties
For example, the attributes of a bishop include its
color (white or black) and its location. The
properties of a character in a role-playing game
may be much more complex, including variables
such as health, strength, dexterity, experience,
level, as well as its location in the online
environment, and even the artwork or other media
associated with that object.
20. Behaviours
The next defining characteristics of objects in a
system are their behaviors. Behaviors are the
potential actions that an object might perform in a
given state.
The behaviors of the bishop in chess include
moving along any of the diagonals radiating from its
current position until it is blocked by or captures
another piece.
21. Behaviours
The behaviors of the role-playing character
described previously might include walking,
running, fighting, talking, using items, etc.
22. Relationships
As we mentioned earlier, systems also have
relationships among their objects.
This is a key concept in design.
If there are no relationships between the objects in
question, then you have a collection, not a system.
23. Relationships
For example, a stack of blank index cards is a
collection. If you write numbers on the cards, or
mark them in several suits , then you have created
relationships among the cards.
Removing the “3” card from a sequence of 12 will
change the dynamics of a system which uses those
cards.
24. Relationships
Relationships can be expressed in a number of
ways.
A game played on a board might express
relationships between objects based on location.
Alternately, relationships between objects might be
defined hierarchically, as in the numerical sequence
of cards described previously.
28. Analysis
Analysing games is easy.
They are controlled systems, in some cases,
obviously signposted.
Applying this to other subjects is hard and requires
careful thought.
29. Analysis
But it isn’t hard.
The Institute of Play teaches Systems Thinking to
6th Graders (11-12 year olds)
They include the following
30. Analysis
Distinguishing what is
important and salient.
Identifying causal
relationships among
things and ideas.
Sequencing causes and
effects to act and think
effectively over time.
Establishing patterns
and relationships over
time and space.
Clarifying disparate bits
of information and
reconciling them to a
larger whole.
31. Analysis
Resolving tensions and
discrepancies within
existing structures.
Explaining knowledge in
terms relative to the
individual whose
discourse is the
reference point.
Providing relevant
examples from other
knowledge bases
that help to demonstrate
and exemplify the
efficacy of primary
knowledge.
Applying knowledge to
new circumstances and
situations.
32. Analysis
Do this all the time, where ever you are.
Analyse shopping, walking to uni, getting on a ferry,
a train, a bus.
Analyse making a coffee.
Because now it gets hard.
33. MDA Analysis
We all know about MDA as a framework, and you
may have used it to analyse games.
But analysis is analysis.
So – how do we use MDA to analyse real world
systems?
34. MDA Analysis
Games can be seen to be developed like this:
DESIGN
Procedures
And play
patterns
Player
Experience
A Design is
written. It
features the
games rules
The player
interacts with
these rules
and develops
play styles
Which
translates
into one of
8 kinds of
“fun”
35. 8 Kinds of Fun
• Sensation: game as sense-pleasure
• Fantasy: game as make-believe
• Narrative: game as unfolding story
• Challenge: game as obstacle course
• Fellowship: game as social framework
• Discovery: Game as uncharted territory
• Expression: Game as soap box
• Submission: Game as mindless pastime
36. Type of Work
• High-stakes work
• Busy work
• Mental Work
• Physical Work
• Discovery Work
• Teamwork
• Creative Work
Jane McGonigal describes Fun in terms of Work.
There are:
37. MDA
This can be analysed down to:
Mechanics Dynamics Aesthetics
38. MDA
• Mechanics: The rules and concepts that formally
specify the game system
• Dynamics: The run-time behavior of the
game-as-system.
• Aesthetics: The desirable emotional responses
evoked by the game dynamics.
39. MDA Analysis
How do we even start?
Aesthetics
Let’s start here. With an emotional response.
40. MDA Analysis
When you are analysing a system to gamify it, you
are looking to engage your players.
Interaction with your system is going to give them
an emotional response.
The chances are they already have one that you
might want to change
41. The Dentist
We all have an
emotional response to a
Dentist trip.
Generally the response
isn’t a good one.
What is the Mechanic
and Dynamic of this?
42. The Dentist
How have we got there?
The mechanic is a
medical professional.
The dynamic might be
where the problem is?
43. MDA Analysis
A Dental dynamic could be the noise of the drill, the
waiting room, the fear of pain?
How do we change this? What elements could we
modify in improve that dynamic?
44. MDA Analysis
When analysing with MDA, you have to broadly
apply terms.
What is the output for the player?
An emotional response?
An effect?
A take-away?
45. MDA Analysis
When analysing with MDA, you have to broadly
apply terms.
How do you define your mechanics?
What are the elements that set up your emotional
response?
What are your basic blocks?
Individual objects? Concepts? The people involved,
or representations/notions of them?
46. MDA Analysis
Then – what is the “The run-time behavior of the
game-as-system”?
This, obviously, requires you to understand the
system and see how the objects work within it.
47. MDA Analysis
Early attempts at this will be long and tortuous.
But it gets easier.
At some point you’ll think about systems/MDA as
second nature.
Until then – practice…
48. The “Out of Left Field” Option
This is Aleister Crowley.
Known as The Great
Beast, the wickedest
man in the world.
He was an English
occultist, ceremonial
magician, poet, painter,
novelist, and
mountaineer
49. Aleister Crowley
He analysed magick and reduced it to an, almost,
post-modern simplicity that wasn’t to be seen
again until the Chaos Magic movement in the
mid-70s, popularised in the 80s
He was also a monumental troll.
50. Aleister Crowley
The reason we’re looking at him today is
because of something he wrote in 1904.
While in the midst of a ritual, and over the course
of 3 days, Crowley heard the voice of Aiwass, an
entity who was the messenger of Horus, or Hoor-
Paar-Kraat.
The voice dictated what became The Book of
The Law
51. Aleister Crowley
It stated that a supreme moral law was to be
introduced in this Aeon,
"Do what thou wilt shall be the whole of the law”
This became the subject of many interpretations.
Some seeing it as a call to just do what you want,
others seeing it, as Crowley did, as living
intentionally
52. Aleister Crowley
Magick is the Science and Art of causing
Change to occur in conformity with
Will. (Illustration: It is my Will to inform the World
of certain facts within my knowledge. I therefore
take "magickal weapons", pen, ink, and paper; I
write "incantations" — these sentences — in the
"magickal language" ie, that which is understood
by the people I wish to instruct;
53. Aleister Crowley
I call forth "spirits", such as printers, publishers,
booksellers and so forth and constrain them to
convey my message to those people.
The composition and distribution of this book
is thus an act of Magick by which I cause
Changes to take place in conformity with my
Will.)
54. Aleister Crowley
Intentional Acts.
Using this view of “living magically” you can
weigh up the actions of your designs.
What are the consequences of choosing one
action over another?
What tools are available for you to cause
change?