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A Richer Understanding of Bayesian Network Diagrams Dr. Kamaran Fathulla University of Essex International Academy [email protected] June 2011 PowerPoint PPT Presentation


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A Richer Understanding of Bayesian Network Diagrams Dr. Kamaran Fathulla University of Essex International Academy [email protected] June 2011. Some 2500 years ago people have used clay tablets to express boundaries, groupings, and routes.

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A Richer Understanding of Bayesian Network Diagrams Dr. Kamaran Fathulla University of Essex International Academy [email protected] June 2011

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A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

A Richer Understanding of Bayesian Network Diagrams

Dr. KamaranFathulla

University of Essex

International Academy

[email protected]

June 2011


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Some 2500 years ago people have used clay tablets to express boundaries, groupings, and routes

Clay Tablet map from Ga-Sur, Kirkuk, 2,500 B.C.


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Diagrams are everywhere

Applied psychology

Cognitive science

Linguistics

Visual programming

Data visualisation

Graphic design

Education

History and philosophy of science

Architecture

Blackwell and Engelhardt (1999)

Eppler, 2003


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

But.... Diagrams continue to be difficult to understand and work with

There has been a substantial growth in the use of diagrams in earlier stages of the research process to collect data. Despite this growth, guidance on this technique is often isolated within disciplines.

Numerous references are cited on people expressing difficulty or discomfort with diagramming.

Muriah J Umoquit, et al (2011) A multidisciplinary systematic review of the use of diagrams as a means of collecting data from research subjects: application, benefits and recommendations


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Sources of the difficulties

1. Diversity of types of diagrams

Boxes and lines

Contour maps

Bar charts

etc..

2. Diversity of types of change (dynamics)

Different types of diagrams require different editing operations

Different diagram types have different rules of well formedness

Need to relax these rules but maintain well formedness

3. All of the above in semantically mixed diagrams

Even single type diagrams have mixed semantics

Next..... Bayesian type diagrams


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Influence Diagrams ID

First introduced in the mid 70s.

ID’s have become a de facto standard for representation of Bayesian decision problems.

There is not too much feedback from analysts and experts about their experiences using IDs for building decision-making models.

Constructing ID is considered as an art.

An Influence Diagram

Concha Bielza, Manuel Gómez, Prakash P. Shenoy (2009)

Modeling Challenges with Influence Diagrams: Representation Issues

SCHOOL OF BUSINESS WORKING PAPER NO. 319


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

On the difficulty of Bayesian Network Diagrams BND

Misconceptions:

No order misconception: Failure to understand that paths

are ordered (e. g., commits CYB).

LAN-as-path misconception: Failure to see that a path

through a LAN visits intervening nodes in sequence, like

a physical path (e.g., commits YRBMC)

Omit bridge misconception/error: Failure to recognize or

list a true bridge node (e.g., commits BC or omits BMC)

Commit bridge misconception/error: Inferring a

nonexistent bridge node, or selectively committing the

LAN-as-path error (e.g., commits YRB)

Reading-order misconception/bias: Listing only forward

paths (misconception); or omitting backwards paths more

often (bias)

Generate all possible shortest paths of information flow for Network 1 and Network 2

James E. Corter et al (2009) Bugs and Biases: Diagnosing Misconceptions in the Understanding of Diagrams. Proceedings of the 31st Annual Conference of the Cognitive Science Society

It has been argued that Bayesian reasoning is counterintuitive. People do not employ Bayesian reasoning intuitively, find it very difficult to learn Bayesian reasoning when tutored, and rapidly forget Bayesian methods once the tutoring is over. This limitation seems to hold equally true for novices and highly trained professionals in a field .

Yudkowsky (2009) An Intuitive Explanation of Baye's Theorem

Where to go from here?


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Pointers for a better understanding diagrams

“Knowledge Representation for people”

Brachman (1985)

“Making Bayesian networks more accessible to the probabilistically unsophisticated”

Eugene Charniak (1991) Bayesian Networks without Tears. IA Magazine


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Towards a new approach

1 - Pointers

Central role of human functioning

D. Watson, Nobel prize winner (1968) (who discovered the structure of DNA): "drawing and thinking are frequently so simultaneous that the graphic image appears almost an organic extension of the thinking process".

  • Everyday diagramming

  • Composed of visual elements which each have some symbolic meaning

  • Expressing several different types of symbolic information

  • Contain a number of mixed visual stylesto express these different types respectively

  • Growing rather than finished

  • Having parts already drawn being replaced by others

  • Part of the thinking is inherent in communication

  • The diagramming process is fluid

Need a meta approach


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Existing frameworks of understanding diagrams

Too fine grained

Too broad/general

Bertin, Peirce, etc

Plastic-robust

Weakly-strongly structured

Abstract-concrete Different meaning - common structure

General-specific

Conventionalizedcustomized

Abstraction

Hidden dependencies

Premature commitment

Secondary notation

Viscosity

Visibility

Closeness of mapping

Consistency

Diffuseness

Error-proneness

Hard mental operations

Progressive evaluation

Provisionality

Role-expressiveness

Creative Ambiguity

Specificity

Detail in context

Indexing

Synopsie

Free rides

Useful awkwardness

Unevenness

Lability

Permissiveness

Programming Salience

Provisionality

Story Content

Modifiability

Controllability

Referencability

Focus

Coordination

Documentation

Consistency

Accountability

Traceability

Visual immediacy

Visual impetus

Visual impedance

Reductionist

Spatial, visual, cognitive, etc..

Eppler et al (2008)


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Towards a new approach

2 – Philosophy

Dooyeweerd's Meaning-Oriented Approach

Dooyeweerd said (1955), "Meaning is the being of all that has been created and the nature even of our selfhood. It has a religious root and a divine origin."

This led him to presuppose that Meaning is the primary property of that is, and that Existence emerges from Meaning.

Meaning is better grasped intuitively, not theoretically.

The ordinary person can understand it in the full, holistic sense of the word


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Towards a new approach

3 – Aspectual Philosophy

Meaning is given to the cosmos (by its Creator) as a framework.

The framework is a framework of law which provides guidance for how all entities function.

Diversity of types of existing based on a diversity oftypes of meaning

This leads into the notion of Aspects

Aspects are irreducible

Aspects are related

Aspects are not absolute

Aspects are independent of things or entities

Aspects are rich


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Qualifying Aspects of Diagrams

Multi Aspectual nature of diagrams


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

What is a SySpM?

A distinct collection of Sy elements, or as

A distinct collection of Sp elements, or as

A distinct collection of M


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Box and Arrow type of diagram

Symbolic Aspect

Primary Sy

1. Items, entities: meaning deliberate intention of creator to symbolize a distinct concept

2. Relationship: deliberate intention of creator to symbolize the idea that two items are related in a meaningful way

Secondary Sy

3. Direction of relationship

4. Relationships can be treated as items

5. Types of items

6. Types of relationship

7. Names to identify the items or relationships


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Spatial Aspect

Primary Sp

1. Boxes: as being present rather than absent.

2. A line which is straight, curved, or bent.

Secondary Sp

3. Boxes shape: this could be a pictogram, a diamond, a square, etc. Box shapes are considered as a spatial sub type of a thing and in this case it is a visual characteristics which may also include color, texture, etc.

4. Box visibility: distinction between 1 and 3 becomes important in software generated diagrams. A box could be present but hidden. Software should be able to understand that an invisible box does not necessarily mean it is not present.

5. Edges of boxes

6. Position of box

7. Size of box

8. Boundary: outside rather than inside. This refers to the spatially defined vicinity of a thing. This is important in cases where a line ends in the vicinity of a box rather than on one of its edges.

9. Route of line.

10. Line texture: this refers to the use of dotted, dashed, thick/narrow features of lines.

11. Connect/Attach: this is about the attachment of a line end to a box in an unambiguous way as opposed to a line being in the vicinity of one box rather than another.

12. Arrow head. These may be of various kinds. They indicate direction of a line.

13. Text. These are used to identify boxes.

14. Empty background (white space): this may actually be decorated as in backdrop.

15. Width of a line.


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

Putting it all together

1. Sp Mapped to Sy

2. Constraints

3. Sp Not Mapped to Sy

Continued


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

4. Handling Change


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

What does this mean for BND’s ?

Bayesian networks are complex diagrams

A definition of BND:

“Bayesian networks are directed acyclic graphs whose:

nodes represent variables, and whose

edges represent conditional relationships between two variables”

1

2

Consider the use of the Box and Arrows diagram type and modify its constraints to reflect BND

Develop BND as a new diagram type.

OR


A richer understanding of bayesian network diagrams dr kamaran fathulla university of essex international academy kamaran essex ac uk june 2011

A typical BND

Box and Arrows diagram

Tabular display


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