A great image “Four types of evidence” appears in a recent paper on probabalistic argumentation schemes¹. The delineation of 4 types of evidence² serves the larger goal of the paper — which is to describe how to combine evidence of different types.

1. ‘Dempster-Shafer Argument Schemes‘ by Yuqing Tang, Nir Oren, Simon Parsons, and Katia Sycara (2013) in Proceedings of ArgMAS 2013.
2. These, the authors mention, were drawn from an earlier technical report: K. Stentz and S. Ferson. Combination of evidence in Dempster-Shafer theory. Technical Report SAND 2002-0835, Sandia National Laboratories, 2002. See especially pages 10-13. The context in that technical report, is sensor fusion using Dempster-Shafer Theory, which as I have since learned, is a common approach to combination of evidence.

Argumentation analysis can be simplified by thinking about the patterns used.
But what are the key patterns? Here are two diagrams showing different views.

Rahwan suggests 5 common basic argument structures: single, linked, convergent, serial, and divergent.¹

Iyad Rahwan. Mass argumentation and the Semantic Web. 2008.

Meanwhile, Wei and Praken give 5 possible argumentative structures that have one or two inferences.²

From Bin Wei and Henry Prakken. Defining the structure of arguments with AI models of argumentation.

Why 5 structures? Five connected structures emerge from having two types of inference — as unit I (single) and unit II (linked) inference. With two inferences of either type, we can make five patterns:³

(1) unit I argument (single)
(2) unit II argument (linked)
(3) multiple unit I argument (serial)
(4) multiple unit II argument
(5) mixed argument

What is interesting is to look at the differences: Rahwen doesn’t cover (4) multiple unit II and (5) mixed arguments. Meanwhile, Wei and Prakken’s list doesn’t include Rahwen’s convergent & divergent argumentation.

So which are the key patterns?

Single and linked arguments are fundamental, and serial arguments are mathematically simple and Rahwen suggests that they are common in use.⁴But the rest?

Convergent & divergent argumentation structures are both candidates: Wei and Prakken don’t cover these, I suspect, since each could be separated into two separate single arguments, which have the same premise (divergent) or conclusion (convergent). These structures can be important in practice: Convergent arguments give multiple reasons for coming to a conclusion — essential when no single reason suffices. The structure of divergent arguments seems to me to be most useful for showing contradictions in diverse conclusions, e.g. for reductio ad absurdum arguments; I’d love a real-world example of a divergent argument where keeping this structure is important.

1. Iyad Rahwan. Mass argumentation and the Semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web, 6(1):29–37, February 2008. http://dx.doi.org/10.1016/j.websem.2007.11.007
2. Bin Wei and Henry Prakken. Defining the structure of arguments with AI models of argumentation. Computational Models of Natural Argument XII at ECAI 2012. Pages 60-64 in Proceedings.
3. Definition 9. The types of arguments can be defined as follows:
   (1) An argument A is a unit I argument iff A has the form B ⇒ ψ and subargument B is an atomic argument B : φ. We call the inference rule φ ⇒ ψ a unit I inference.
   (2) An argument A is a unit II argument iff A has the form B1,…,Bn ⇒ ψ and subarguments A : B1,…,Bn are atomic arguments B1 : φ1 ,. . . ,Bn : φn . We call the inference rule φ1,…,φn ⇒ ψ a unit II inference.
   (3) An argument A is a multiple unit I argument iff all inferences r1, . . . , rn in the argument A are unit I inferences.
   (4) An argument A is a multiple unit II argument iff all inferences r1, . . . , rn in the argument A are unit II inferences.
   (5) An argument A is a mixed argument iff A has at least one unit I subargument and unit II subargument.
   We display the diagrams of argument types in Figure 3. For simplicity, we assume n = 2 in these diagrams and show only one case of a mixed argument.
4. Statistics on argument use would be valuable, but we have limited information about this. Aracuaria DB? Output from argumentation mining?

Jason Priem has a wonderful slidedeck on how to smoothly transition from today’s practices in scientific communication to the future. Here is my reading of the argument given in Jason’s slides:

Communicating science is a central and essential part of doing science, and we have always used the best technology available.
Yet currently, there are several problems with journals, the primary form of scholarly communication.

Journal publication is

• Slow
• Closed
• Hard to innovate

and has

• Restrictive format: function follows form
• Inconsistent quality control

These problems are fixable, if we realize that journals serve four traditional functions:

1. Registration
2. Archiving
3. Dissemination
4. Certification

By decoupling these functions, into an a la carte publishing menu, we can fix the scholarly communication system. Decoupled scholarly outlets already exist. Jason mentions some outlets (I would say these mainly serve registration functions, maybe also dissemination ones):

• ArXiv
• Math Overflow
• SSRN
• Faculty of 1000 Research
• the blag-o-sphere

Jason doesn’t mention here — but we could add to this list — systems for data publishing, e-science workflow, and open notebook science; these may fulfil registration and archiving functions. Also, among existing archiving systems, we could add the journal archiving functions of LOCKSS is the main player I’m familiar with.

To help with the certification functions, we have altmetrics tools like Impact Story (Jason’s Sloan Founded project with Heather Piwowar).

Jason’s argument well worth reading in full; it’s a well-articulated argument for decoupling journal functions, with some detailed descriptions of altmetrics. The core argument is very solid, and of wide interest: Unlike previous articulations for “pre-publication peer review”, this argument will make sense to everyone who believes in big data, I think. There are other formats: video of the talk¹ and a draft article called “Decoupling the scholarly journal”².

—
Briefly noted in some of my earlier tweets.

1. Thanks to Siegfriend Handschuh, who suggested the video of Jason giving this talk at Purdue.
2. by Jason Priem and Bradley M. Hemminger, under review for the Frontiers in Computational Neuroscience special issue “Beyond open access: visions for open evaluation of scientific papers by post-publication peer review”

Last March¹ I gave a reading group talk about knowledge representations of online disputes:

Titled “Turning social disputes into knowledge representations”, the talk was based primarily on two papers:

• Toni and Torroni. Bottom-up Argumentation. In: First International Workshop on the Theory and Applications of Formal Argumentation 2011 (TAFA), 16-22 July, 2011, Barcelona, Spain. http://www.doc.ic.ac.uk/~ft/PAPERS/tafaPT.pdf
• Benn, Buckingham Shum, Domingue, and Mancini. Ontological Foundations for Scholarly Debate Mapping Technology. In: 2nd International Conference on Computational Models of Argument (COMMA ’08), 28-30 May, 2008, Toulouse, France. http://oro.open.ac.uk/11939/

Online argumentation, and particularly knowledge representation from argumentation, is the overarching theme of my dissertation at DERI and as I get together the overall argument, I’ve been looking through my old slidedecks. My previous reading group talk, from November 2011, was about Using Controlled Natural Language and First Order Logic to improve e-consultation discussion forums, based on several papers by Adam Wyner and his colleagues; more recently Adam and I have started a fruitful collaboration, funded in part by the COST action on argumentation and a Short-Term Travel Fellowship from Science Foundation Ireland.

1. March 28, 2012

Converting temperatures in your head is a good trick for Americans living abroad.

So here’s the trick. You memorise the following correspondences:

0 °C = 32 °F
10 °C = 50 °F
20 °C = 68 °F
30 °C = 86 °F
Then, to convert any temperature that is near these, approximate 1 °C = 2 °F. This will allow you to convert almost any naturally occurring outdoor temperature in the UK in either direction to within 1° accuracy.

Let’s try it. As I write the current temperature in Edinburgh is 14 °C. This is 10 °C plus 4° extra. From memory convert the 10 °C to 50 °F. Then convert 4 °C extra to 8 °F extra and add it back on. This gives you 14°C = 58°F. This is not exact, but close enough that you know to wear a jumper. The exact formula is

14 * 9 / 5 + 32 = 57 F
Good luck doing that in your head.

from Charles Sutton’s Converting Fahrenheit into Celsius.

A jumper, for Americans, is “A pullover sweater.”

I’m still looking for real-time collaboration tools for LaTeX. I need to try shareLaTeX again. Sadly, LaTeX-lab (which layers ontop of Google Docs) is only designed for a single editor at a time (kind of defeating the purpose). Apparently, ScribTeX (discovered via pinboard search) is popular (and there’s also verbosus) — and sounds useful.

One of the sticking points of using Google Docs (which is useful at some points of the editing) was its use of smartquotes. That, at least is avoidable: Tools -> Preferences gives the option to disable smart quotes and automatic substitution.

Google Docs preferences - disable smart quotes

James Malone reflects on a panel discussion on evaluation and reuse of ontologies. He wants there to be a “a formal, objective and quantifiable process” for “making public judgements on ontologies”. Towards that, he suggests that we need:

1. A formal set of engineering principles for systematic, disciplined, quantifiable approach to the design, development, operation, and maintenance of ontologies
2. The use of test driven development, in particular using sets of (if appropriate, user collected) competency questions which an ontology guarantees to answer, with examples of those answers – think of this as similar to unit testing
3. Cost benefit analysis for adopting frameworks such as upper ontologies, this includes aspects such as cost of training for use in development, cost to end users in understanding ontologies built using such frameworks, cost benefits measured as per metrics such as those above (e.g. answering competency questions) and risk of adoption (such as significant changes or longer term support).

- James Malone, in Why choosing ontologies should not be like choosing Pepsi or Coke, about his International Conference on Biomedical Ontology panel ‘How to deal with sectarianism in biomedical ontology.

• Touched, not typed
• Sent from my $DEVICENAME
• Consider any misspellings my gift to you
• Typed with thumbs
• Sent with mobile solution
• Sent from a mobile operating system. Which one isn’t of any importance to you, the receiver. However, if you feel that knowing this detail would affect positively your reading of this email you can, of course, ask me.
• Sent from my smartphone platform of choice….hint not a fruit
• I prefer robots to fruit.
• Fruits are for fruitcakes, Robots are for emailing.
• bots best for smart phones
• Smart fruit is an oxymoron
• Sent via a really tiny keyboard
• Sent from a mobile device. Erroneous words are a feature, not a typo.
• Sent from mobile; pls excuse typos
• $DEVICENAME = specific mobile operating system of choice
• Sent from my stationary operating system of choice.
• Erroneous words are a feature, not a typo.
• (Short, curt and ill-formed message sent from my portable telephone machine.)
• > Sent wirelessly from my BlackBerry device on the Bell network.
  > Envoyé sans fil par mon terminal mobile BlackBerry sur le réseau de Bell.
• *Sent from a mobile phone – please excuse the brevity of the message
• via small communication device/pardon random autocorrects and fat finger typos.
• Warning: I either dictated this to my device, or I typed it clumsily. Expect typos and weirdness.
• Sent from a mobile device. Excuse brevity and typos.
• Typed by thumbs and sent by my Verizon Wireless gadget
• Sent from a mobile device. Please excuse brevity and tpyos.
• Sent from my Verizon Wireless 4G LTE smartphone
• Sent from tiny touchscreen gizmo, excuse any auto correct nonsense that slips in…
• Sent from my rotary phone
• Sent with my thumbs (Thanks to Andy Powell.)
• sent from my shoe (Thanks to Larry Hynes.)
• Sent while walking into stuff(Thanks to Ryan Sarver (via Laura Dragan and Tim O’Reilly; used by David Cohen)

–
Previously discussed on Twitter (thanks to David Crowley and Becky Yoose for spreading my question). Apparently desktop users want forgiveness too.

Increasingly, I’m using Google Docs with collaborators. Yesterday, one of them pointed out the new “Research” search tab within Google Docs. (Tools->Research). I’m a bit surprised that your searches don’t show up on your collaborators’ screen. I’m particularly surprised that sharing searches doesn’t seem possible.

Google Docs' new 'Research' tab promotes search within Google Docs.

Apparently, it is pretty new. More at the Google Docs blog.

One thing I can say about Kindle: error reporting is easier.

You report problems in context, by selecting the offending text. No need to explain where - just what the problem is.

Feedback receipt is confirmed, along with the next steps for how it will be used.

By contrast, to report problems to academic publishers, you often must fill out an elaborate form (e.g. Springer or Elsevier). Digging up contact information often requires going to another page (e.g. ACM.). Some make you *both* go to another page to leave feedback and then fill out a form (e.g. EBSCO). Do any academic publishers keep the context of what journal article or book chapter you’re reporting a problem with? (If so, I’ve never noticed!)