Every real story has at least two sides. To tell it fairly, there is a need to create a balanced, equitable structure to see where the situation stands and compare apples to apples. Too bad there are no data structures made like coloring books so you could fill in the blanks.
Setting up an equitable structure to examine two sides of an argument is a challenge in itself. One example of instructions for creating symmetrical, even opposing structures is here copyright ? 2003, Zef Damen, The Netherlands. His reconstruction of the Milk Hill 17-06-2003 formation is below. But pretend this unfolding logical structure was used to examine a touchy subject, see both sides of a story, set up and compare histories and facts…
Step 1 or 27 – place opposing view points into the same structure, begin to divide and classify. One side is for an issue like the war in Iraq, the other side is against.
Step 9 of 27 – more and more details, connections, and separations are needed between the opposing histories, available records, and points of view. There are more sides to the argument because it is getting more complex. There are more factors to consider, deciding a stance on large impact issues like wars is not simple.
Step 27 of 27 – a complex situation is depicted, as many view points as possible given equal space.
Once the structure reaches a limit of the number of facts that can be isolated and examined together in one group – the confusion of messy reality can be methodically broken down into manageable parts to systematically destroy weak arguments and keep only the best examples.
Systematically eliminate directly opposing views requires a formalized process of elimination that is best done by people. But how could machines help us find the fallacies? A taxonomy of fallacies is defined by Gary Curtis, Logic at Indiana University, Ontologist for Cycorp
What if proper fair layouts of emotional and expensive real world situations could be made in order to fill in the blanks to illustrate the many side of any issue – and computers could be taught to recognize what doesn’t actually fit or belong in certain areas? Ideally, by people and machines working together, eventually opposing, extreme, and just plain wrong views will eventually cancel each other out. The truth will be exposed because that is all that would be left. Once the information was clean, accurate and diverse – then you could really pick up a problem to start examining it and see various relationships from every angle.
Then structures even simpler than coloring books would be needed. For example FLIPP explainers by David Cox where “notice we would see (in this case 11) patterns even if no subject matter were shown.
In other words, the FLIPP explainer is a formalized logical structure to arrange and look through potentially huge amounts of information. If any layout from simple to complex was made to sort through and present the paintings a person or culture prefers, could the same layouts be reused for different subjects too?