And a quick pitch: If you know someone who's hiring in those areas then please give me a ring. (I'm looking for the intersection of progressive work, software tool development, and solid funding.)

Josh Silver (Represent.us)

I had the good fortune to meet Josh Silver, who has made contributions in media reform and campaign finance reform (think Citizens United). He currently runs Represent.Us, and, before that, Free Press. Josh summarized his perspective on what he sees as the two main challenges to policy change (an informed public, and government representatives who actually represent people, not big money or corporations) and gave me a precis on how money is injected into politics (direct to campaigns and super PACs, which can be tracked, and indirect via 501c4 and 501c6 organizations, which is not directly tracked - more here). Josh said there's a lot of activity in data and politics, for example:

• Sunlight Foundation
• OpenSecrets.org: Money in Politics -- See Who's Giving & Who's Getting
• U.S. Congress Campaign Contributions and Voting Database | MapLight - Money and Politics
• Follow The Money
• And of course the awesome @nytgraphics work.

He also showed me the Greenhouse browser extension, which rewrites web pages via some information extraction to pop up campaign finance information for people on the page.

We talked a little about the fallibility of human nature and how emotion and stories often trump facts (It's a "basic human survival skill," explains political scientist Arthur Lupia of the University of Michigan. We push threatening information away; we pull friendly information close. We apply fight-or-flight reflexes not only to predators, but to data itself.). (I tend to arrogantly think I'm immune, but I'm not - see Thinking, Fast and Slow and You Are Not So Smart - Amazon links)

Regarding funding for software development, Josh suggested either contacting one of the above organizations, or propose funding for a grant from someone like the Knight Foundation.

I finished up by talking a bit with one of his research assistants about possible tools that could help folks like him and reporters collect campaign contribution information more easily. My takeaway was that there's no good, central aggregator; information is disparate and spans multiple sites.

Great stuff! Stay tuned...

Areas of Interest

• Skepticism, critical thinking
• Journalism, media reform
• Education and science
• Fact checking
• Environment, climate change
• Freethought
• Politics, digital government, campaign finance reform
• Citizenship, participation, digital government

Last night I attended a favorite libraries Python Meetup organized by The Boston Python User Group (@bostonpython) and hosted by Akamai. Here I'll give a brief summary of the libraries covered, and a newcomer's perspective on the event. The presentations overall were great - focused, not too long, with code examples and demos. And of course the theme was a winner - who doesn't love learning about a tasty library that may come in handy? Fun demos make it even better. I'm looking forward to attending more of them.

Neil Tenenholtz: mrjob

mrjob is a library for writing MapReduce jobs (Hadoop tutorial [here](https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.htm l), original Google paper here). Neil did a live demo showing how little code is needed to express paralellizable tasks, at the simplest, just a mapper() and reducer() function. He demo'd wordcounta and showed how to run it on different clusters, including simulated, local, and Hadoop or Amazon Elastic MapReduce installations. At one of the UMass labs I worked in, we used Hadoop in a number of ways, including batch data processing and cleanup (i.e., ETL) for the machine learning algorithms, and parallelized SQL via Cloudera Impala. Yummy stuff!

Lindsay Raymond: Funcy

Funcy adds to Python some functional tools "inspired by clojure, Underscore.js, and [his] own abstractions." (They reminded me a bit of the Scala idioms I saw in my brief exposure to it for a Spark GraphX project I did to implement fast graph path searches. Ask me sometime about my take on Scala as compared to Python ;-) Lindsay gave some examples, and it was clear that there's a lot more to the library than can be shown during a short demo, but she touched on concepts like how readable elegantly composed functions are, and how testing is eased by breaking computation down into stand-alone pieces. (As an XP fan, I really appreciated this point.) While I've done less functional programming than perhaps I'd like, funcy got me interested in learning more.

Scott Sanderson: Click

Click is a package that simplifies writing command-line interfaces. Scott demonstrated (with humor :-) the main features, showing the decorator-based style it uses. Oftentimes I end up writing command line tools for applications that don't need a UI or for ones that can be scripted, and I'll definitely have a look at Click the next time the need comes up. You can read the author's motivation for writing an alternative to the inbuilt argparse module here, and learn how it compares to others at Comparing Python Command-Line Parsing Libraries - Argparse, Docopt, and Click.

Amandalynne Paullada: NLTK

Natural Language Toolkit is a new favorite of mine, and I was excited to see Amandalynne's presentation. The library is rich in features, including word and sentence segmentation, part-of-speech tagging, classifiers, information extraction, and more. Amandalynne demo'd a clever nickname generator (e.g., https://en.wikipedia.org/wiki/Text_segmentation) which, as you'd expect, was a hit. Even (or especially) when it surprised. I'm currently evaluating it for a skeptical toolbox idea I have, using it for named-entity recognition, for example. (I learned a little about this area in my last contract; it's surprisingly difficult.) There's a very well done NLTK book, which I came away from reading thinking that it would be a great introduction to Python for newbies. This is because learning the language is set in the context of text processing, which is inherently cool. Good stuff.

Ned Jackson Lovely: itsdangerous and bcrypt

itsdangerous is a library for doing cryptographic signing, and can be applied to cases like secure "amnesia" ("forgotten password") emails or saving protected cookies. Using the module this way can, in some cases, eliminate the need to store hashed data in server-side tables. bcrypt is a tool for password hashing. Not having done work in this area, I can't say much about these libraries, but I fully believed Ned's point about DIY cryptography being hard to get right, and about getting professionals involved for anything important, such as protecting credit card information.

Anyway, the project is on GitHub at http://github.com/matthewcornell/arduino-relaxation-tool. Below I've included the readme along with some pics. Enjoy!

Circuit

This is basically a mashup of two standard Arduino projects - reading analog values from a pot, and controlling a piezo motor. The circuits are duplicated for left and right sides. The circuit diagram is here, and a picture of the assembled board is here.

Here is an example of the individual projects:

• Reading a pot: Reading a Potentiometer (analog input)
• Driving a piezo motor: Figure 8-8 of the Arduino Cookbook (Amazon link here).

Parts

• 2 x 1K Ohm resistors
• 2 x 2N2222 transistors
• 2 x 1N4001 diodes
• 2 x 0.1uF capacitors
• 2 x piezo motors, e.g., Micro Vibrating Motor Cell Phone Beam Bristlebot Robot 6x10mm
• 2 x 33 Ohm resistors
• 2 x 10K Ohm circuit board pots

Assembly

I used a basic Arduino breadboard - it was pretty straighforward. I didn't have any 33 Ohm resistors so I had to use 3 x 10 Ohm ones in series. For the motors I simply applied some heat shrink tubing for something to hold onto. I looked into more durable cases (I tried ping pong balls), but this worked OK for the prototype. See the pic here.

Code

The code manages four pins - two analog in ones for reading the pots, and two digital out ones for controlling the motors. The fifth pin is the built in LED digital out one that I pulse for effect. The pots are read by adjustPeriodAndDurationBasedOnPots() and saved into two variables: vibePeriod (how often the motor vibrates in ms: a range ~ [100 ms, 2000 ms]) and vibeDuration (how long the motor vibrates, in the same time range). The last variable is isCycleLedOn, which alternates between 0 and 1 with each cycle. After setup() initializes the pins, The standard Arduino loop() function just reads the pots (which sets the two control variables), cycles the first motor, and repeats for the second one. cycleMotor() turns on the motor, cycles the LED, waits the appropriate duration, then repeats to turn the motor off. Finally, adjustPeriodAndDurationBasedOnPots() reads the analog value from each pot (a value between 0 and 1023) and scales it to a range between ~100ms to 2000ms, which seemed to work pretty well. (Any faster was irritating, and any slower would have been boring :-)

Following is some detail from the README on the GitHub fork I created. You can play with it at annotator-demo.herokuapp.com.

          1: set current type to Title
 shift  + 1: filter all but Title
          2: set current type to Abstract
 shift  + 2: filter all but Abstract
          3: set current type to Author
 shift  + 3: filter all but Author
     Escape: reset filter

+: create new annotation using current type

                     arrow key: move selection 1px
            shift  + arrow key: move selection 10px
           option  + arrow key: resize selection 1px
 shift  +  option  + arrow key: resize selection 10px

backspace|delete: delete selection
    control  + d: duplicate selection

          tab: select next
 shift  + tab: select previous

x: display text for selection

What finally kicked me in the butt were two things: 1) The publishing of Google's paper Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources (which got a good bit of coverage), and 2) the recent Committee for Skeptical Inquiry article Online Tools for Skeptical Fact Checking by Tamar Wilner. To that end I've created a GitHub project that at the moment has two files: the proposal itself and a somewhat-organized XMind mind map file skeptical-toolbox.xmind that lists some detail.

As I said in the Implementation section, the tools cover a range of complexity, which affords our quickly rolling out something useful using the simpler ones, and progressively introduce more sophisticated tools as the toolbox develops. I plan on using Python + Flask to write it in (same as I used for PeepWeather), but I'm quite open to other languages and frameworks, such as Ruby on Rails if someone steps up and convinces me. One thing in Python's favor is the popular Natural Language Toolkit with its information extraction tools, especially around entity recogition.

What do you think? It is very early days (day zero, I suppose) but I hope that sharing this will generate some thoughts. If you're interested in helping build this - awesome! Just comment here or send me a line.

(Image: Memory Belt)

In those four years I got a lot done, including exploring ways to scale up the Ph.D. students' algorithms [1], coaching students in the Extreme Programming agile software development methodology that I love so much, implementing lab infrastructure improvements (I consolidated multiple aging servers into a single modern cluster, and got the lab using the commercial wiki, Confluence), and of course collaboratively designing and coding the lab's new causal learning in Python (a language I love, BTW, though I still enjoy Java).

As a pleasant surprise, before the funding ended in May, at the last minute another CS professor asked me to help his lab get some urgent work done during the summer, which I was happy to do. While there I benchmarked and started an optimization effort for their new information extraction pipeline, led the team in meeting a crucial grant deadline, and prototyped a web-based PDF annotation tool [2] for creating a gold standard to evaluate their algorithms' performance against. I was introduced to Node.js, continued learning Scala (I wrote some for my GraphX work), and JavaScript for the web app.

Looking forward, I've started my job search for my next project, with the goal of finding work that's challenging, engaging, and meaningful. I've not had to do one in a while, so the process itself is a kind of experiment. What approaches will work? What is out there? Who is doing cool work? Exciting, and a little scary.

So: if you know someone looking for a versatile software engineer with lots of experience who writes excellent code, then please - drop me a line. My LinkedIn profile is linkedin.com/in/matthewcornell.

• [1] I explored multiple MPP approaches including single-node SQL using PostgreSQL, distributed SQL using Impala on the Hadoop ecosystem and Vertica, and graph databases such as GraphX, Neo4J, and Giraph.
• [2] The prototype allows users to load a PDF file as a browser-renderable SVG, draw and edit rectangular regions overlaid on the text, and save and load them from a server. I used the standard web technologies of JavaScript and JSON, all running on Play Framework and its Java API. I used Fabric.js for the direct manipulation UI, which I really enjoyed working with (check out the awesome demos).

(Image from Fountain on Boston Common.)

I had a great time meeting the challenge of putting together a presentation that had breadth and was motivating, but at the same time understandable to the audience (with a the right amount of stretching to keep them engaged). It went well, with some excellent questions during and after the talk. Here I want to share a little about the concepts and technology I covered in case you're considering giving a similar presentation of your own site. For the curious, here's the PowerPoint as a PDF (unfortunately the video did not work out).

Technologies

I named these technologies, describing each pretty briefly, except for diving deeper into Flask:

• Python web apps: Flask, standard web tech (HTML, CSS, cookies, JavaScript), and the common JavaScript libraries: jQuery for sanity, Bootstrap for layout, and typeahead.js for autocompletion.
• Application-specific: XML and REST (for the weather.gov API), Pillow for the widget image, PyEphem for the daylight calculation, and Weather Icons.
• Deployment: Heroku for hosting, Git for pushing, gunicorn for Flask on Heroku, and Namecheap.com for domain registration.

Concepts

I covered the following at various levels of detail:

• Web frameworks (routing, templates)
• The Model-View-Controller design pattern (and design patterns in general)
• Object-oriented design using Class-Responsibility-Collaborator cards
• Test-driven development
• User interface design

The Code

I jumped into the code at the appropriate points, folding as needed so I didn't overwhelm. I stuck to high level basics, highlighting the concepts they currently know including classes and methods. Mainly the focus was on how the concepts got implemented in Python, but no significant code detail; there simply wasn't time. (Note that IntelliJ IDEA's presentation mode worked great for the projector.)

Motivation

I tried to make two points to get the students excited. First, they are programmers and so they have a powerful skill to create web sites! I suggested they pay attention to any thoughts that come up like "Wouldn't it be great if you could __?" or "Why isn't there a site to __?" Second, I pointed out that putting together even a small site is a fantastic portfolio piece that impresses prospective employers. It shows that they can have ideas and, better yet, bring them into reality on their own. I wrapped up by saying that I had a ton of fun writing PeepWeather and learning the various technologies. It's extremely gratifying to bring something into reality (if that's the right word for something as virtual as a web site :-)

Overall I enjoyed both the talk's preparation and presentation, and I hope to give it at the other Five Colleges.

There are very good reasons why it is in the top 10 in software design at Amazon: After 1/2 way through, I've found the book is unique and very well-written. (I am not alone in this assessment [2].)

It is unique because it goes into a level of detail about coding that is specific and really deep. McConnell has given incredibly clear thought into the minutae of programming, and then shared it with fine writing. My boss chucked when I showed him that there are four chapters (table of contents here) on variables, one dedicated solely to naming them. Awesome! I've been writing software for a long time, and it was gratifying to find that McConnell has put into words things learned intuitively, such as Think about efficiency:

... it's usually a waste of effort to work on efficiency at the level of individual routines. The big optimizations come from refining the high-level design, not the individual routines. You generally use micro-optimizations only when the high-level design turns out not to support the system's performance goals, and you won't know that until the whole program is done. Don't waste time scraping for incremental improvements until you know they're needed.

But beyond being reminded of these (and there are many, many of them, for example: "Initialize each variable close to where it's first used"), a big payoff is the new-to-me ones, of which there are many. The book is simply too detailed to summarize the main points, so I'll just share some tidbits that jumped out at me:

• "Upstream Prerequisites" ("programmers are at the end of the food chain. The architect consumes the requirements, the designer consumes the architecture, and the coder consumes the design.") His point: "the overarching goal of preparation is risk reduction. By far the most common project risks in software development are poor requirements and poor project planning." (Note: I found the StackOverflow question Software design vs. software architecture answered terminology confusion I had about those terms: "I think we should use the following rule to determine when we talk about Design vs. Architecture: If the elements of a software picture you created can be mapped one to one to a programming language syntactical construction, then is Design, if not is Architecture.")
• Programming IN a language (limit thoughts to constructs the language directly supports) vs. INTO a language (decide what thoughts you want to express then determine how to do so using the language's tools).
• "Wicked problems" (like software design): can be clearly defined only by solving it or solving part of it.
• "Design is a sloppy process": is about tradeoffs and priorities; involves restrictions; is nondeterministic; is a heuristic process; is emergent.
• Software's Primary Technical Imperative: the importance of managing complexity by dividing a system into subsystems. "The goal of all software-design techniques is to break a complicated problem into simple pieces. The more independent they are the better."
• For the sake of controlling complexity, you should maintain a heavy bias against inheritance (and prefer containment): Inherit when you want the base class to control your interface. Contain when you want to control your interface.
• Aim for loose coupling (the manner and degree of interdependence between software modules) and strong cohesion (the degree to which the elements of a module belong together).
• Routine length (he generalizes procedures, functions, and methods as "routines"): Allow them to grow organically up to 100-200 lines (excluding comments & blank lines). (My routines tend to be smaller - I'll have to give this more thought.)
• Routine parameters: pass <= ~7 of them. More implies too much coupling.
• Design Practices - Capturing Your Design Work: code documentation, wiki, email summaries, camera (whiteboard) vs. a drawing tool, flip charts, CRC cards (I love 'em), UML diagrams.
• The Pseudocode Programming Process: It was helpful to see this named and formalized. "Once the pseudocode is written, you build the code around it and the pseudocode turns into programming-language comments. This eliminates most commenting effort. If the pseudocode follows the guidelines, the comments will be complete and meaningful." Cool!

And many more. Bottom line: If you are serious and committed about improving your craft, read the book! How about you? What coding books have helped you be a better programmer?

[1] There were two reasons my first two attempts to read this hefty 900-pager failed. 1) I wasn't fully committed, and 2) I didn't have a concrete plan to read it. I resolved the former when I realized I'd been focusing on my work projects to the exclusion of professional development [3] (other than reading a handful of blog posts a week) and needed to shake things up. The second block was simpler to address - break the book up into manageable chunks and commit to reading one every day. I summarized this generally in Reading Books The GTD Way, but in this case I did a back-of-the-napkin analysis, working backward from an estimated chapter velocity:

900 pages, 35 chapters

• 1 chapter/hr -> 35 hours
• 1 hour/workday, 3 workdays/week -> 3 hours/week
• -> ~12 weeks (~2.8 months)

I've been recording my minutes/page progress, which ranges between 1 and 2 1/2. With an average chapter being about 30 pages, this works out to be about 60 minutes/page. Good estimate! (Actually, I'm readming more chapters than three per week, and at my current rate I will be done at the end of April.)

[2] A helpful review was Matt Grover's blog: Code Complete Review, which pointed me to Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin (Amazon link here). That book looks excellent too, though it apparently has less detail, weighing in at a svelte 500 pages.

[3] I was surprised to find few articles on the Web on the importance of professional development for software engineers, at least via a quick Google search. The main one was 10 Professional-Development Tips for Programmers. It is structured annoyingly as a slide show, meaning you have to click through to see the content (an SEO and advertising gambit?), so I've collected the titles for you:

• Staying Current Requires Continuous Learning
• Problem-Solving Skills
• Communication and People Skills
• Networking and Personal Branding
• Code Documentation and Neatness
• Master Naming Functions
• Get Familiar With Agile
• Get Familiar With a Native Mobile Platform
• Project Management Skills
• JavaScript, CSS and HTML5 Skills

Searching the two programming-specific sites I like (Hacker News and reddit.com/r/programming - what are your favorites, BTW?) was more fruitful. Here are a few:

• work experience - Professional development, is wide or deep knowledge better?
• A Simple Tool to Find Your Professional Development Sweet Spot
• Using Peer Code Reviews as a Professional Development Tool
• Ten Simple Tips To Become A Valuable Software Professional
• Staying Sharp: Maintaining Your Development Skills

Right now I'm exploring ways to restart this interest, and I hope that posting these archived ideas here might jiggle something loose. Cheers!

References:

• [1] Google wants to rank websites based on facts not links
• [2] Navigating Controversy as a Complex Search Task

1. Increased Corporate Influence of the Federal Government

Specifically, using technology to enable citizens to monitor for abuses and actions that limit its effectiveness. The first example that comes to mind is the commercialization of legislation: Wealthy corporations and individuals who influence laws to their benefit [1]. The result is laws that ignore the greater good (which is usually in the longer term) in favor of shorter term creation of profits. What can our group do?  Let's apply our ideas to the job of examining this cycle of influence. It seems that the data are available and amenable to relational analysis. Is the problem challenging? I'm not sure; it depends on how clear the patterns are.

References:

• [1] Bundles of Influence

2. Increased Bias in Consolidated Media Companies

As citizens I believe we are facing challenges to fundamental aspects of our society as a result the the recent creation of large media conglomerates that control a high percentage of mainstream media [1]. The problem here is that corporations are biasing the news in directions that favor profits over journalism. What can we do? One idea is to create a system that can indicate the bias of news stories. I imagine something like Google News that lists major news stories along with a bias indicator. The hope is that people reading their favorite writer on a topic will be able to get a summary of the story's bias. A final idea would be to look for stories that are *not* covered by mainstream media, i.e., an automated version of [2].

References:

• [1] Bill Moyers' Buzzflash interview on Media reform
• [2] Project Censored - The News that Didn't Make the News and Why

3. Reduced Rigor in Major Media News Reports

Another problem related to 2) above is to address the current propensity of some writers to report facts as taken from second sources without investigating the original sources [1]. To solve this we might analyze stories written about a single news event in order to determine what sources they derived their information from. For example, do they all link to a single source, or are sources more diversified? Also consider the recent avalanche of stories (many misinformed) about DARPA's Total (now 'Terrorism') Information Awareness program. Could a software system using our research have been able to determine that a small number of influential stories was propagating around the net with little additional analysis performed by those referring to them? Finally, another idea is to create automated versions of some of the 'fact check' web sites currently available ([2], [3]).

References:

• [1] Mainstream Media Fails Itself
• [2] Center for American Progress: Claims vs. Facts Database
• [3] Annenberg Political Fact Check

Some examples are The GOP’s climate change skepticism, in one groan-worthy video, Faulted for Avoiding ‘Islamic’ Labels to Describe Terrorism, White House Cites a Strategic Logic, and even my local paper has some doozies, e.g., Tony Robinson shooting: Protestors hit Madison, Wisconsin streets (video) (with gun control at least being a bona fide controversy).

I am just starting to look into the research around "hindrances due to basic human limitations" (check out Table 1 [2] from A Practical Guide To Critical Thinking) and I'm undecided whether it's possible to change minds through technical means (e.g., requiring comment authors to express some kind of well-structured, if simple, argument along with each claim), but it struck me that the thumbs up/down voting feature that's common to most of these sites exacerbates the problem. Let me take a naive look at this, the most meager form of interaction (clicking is literally the least you can do to interact) and maybe re-evaluate the value of popularity's limited usefulness on the web.

I think at its most basic, the thumbs signal expresses one person's opinion of a human artifact (person, place, thing, or utterance, say) where likability, approval, and desire are common interpretations. As a web tool, Thumbs Up/Down Style Ratings says to use it when "A user wants to express a like/dislike (love/hate) type opinion about an object (person, place or thing) they are consuming / reading / experiencing," with the value being:

these ratings, when assessed in aggregate, can quickly give a sense for the community's opinion of a rated object. They may also be helpful for drawing quick qualitative comparisons between like items (this is better than that) but this is of secondary importance with this ratings-type.

From the individual's perspective, I can see a few uses in online discussions:

• the satisfaction of expressing one's opinion,
• the ability to get attention from others,
• being able to see how others voted, which might lead to
• challenging, or more likely reinforcing, ones opinions and beliefs, and
• finding others possibly in your tribe (and perhaps more importantly, those not in it)

I suppose that from the site's perspective, the feature's value is engagement; people are excited by the above uses.

Regarding quality of discourse, what is the value of this feature? And how does it enhance or hinder critical thinking? To my mind, its contribution is all negative because opinion does not equate to fact. (However, I think I'm out of sync on this, given the current cultural and political climate where opinion is perhaps valued higher than fact.) Let's simplify the question and phrase it in terms of popularity. What is the value of popularity in online discourse? My thinking is, none. A solid argument is not based on personal feeling, it is based on evidence, support, quality of sources, etc. This is literally (well, should be) grade school material.

It's a problem, and my question is, can we replace the thumbs signal interaction feature with a better one, something that ideally counteracts the echo chamber effect? A nascent thought I had was to keep the voting feature but apply it to portions of explicitly-presented logical arguments instead of the individual unstructured utterances that currently make up comments. Briefly, this would require that comments link to an unbiased and well-structured argument (I list some sites below [3]), and it is there, rather than the comments themselves, that people would vote. However, the difference is that they'd be voting on specific elements of the argument (such as a source's trustworthiness) which we might be able to interpret as a self-disclosed bias, perhaps proudly proclaimed.

Without getting into more detail here (I just wanted to get the thumbs up/down limitations written up here), I wonder if sharing and comparing biases instead of opinions might lead to tools to help bring a tiny fraction of people a tiny bit closer to understanding each other. Instead of "Hey John, check out the asshole liberal comments on this article," would it be useful to hear "Hey John, here's my opinion on this article's argument"?

Again, I realize I'm fighting human nature [2], but what if a computer program could analyze two opposing parties' beliefs and, for example, find an area of the argument they agree on? Or find a slightly diverse group of people whose beliefs are different but close (think of a belief search space) and somehow bring them together.

Naive? Probably, and this post is rough, but I'd love to hear your thoughts on any of it.


[1] I should put "controversial" in quotes for those topics like climate change that are no longer controversial from the scientific - i.e., reality-based - perspective. And I am quite looking forward to watching the Merchants of Doubt documentary (Rotten Tomatoes review and Amazon book links), by the way. Seen it yet?


[2] From A Practical Guide To Critical Thinking: Hindrances Due To Basic Human Limitations:

• Confirmation Bias & Selective Thinking
• False Memories & Confabulation
• Ignorance
• Perception Limitations
• Personal Biases & Prejudices
• Physical & Emotional Hindrances
• Testimonial Evidence

[3] Argument tools:

Debatewise - where great minds differ

• Justify
• Argunet Editor - Argunet
• Debatepedia, the Wikipedia of Debates
• DebateGraph
• ProCon.org - Pros and Cons of Controversial Issues
• TruthMapping