In our blog post Daily Thinking – Discovering Patterns we showed some alarming daily thinking habits, like assuming that there is a linear, unambiguously relation between a cause and an effect. For example, it is assumed that increasing e-commerce will reduce traffic. People will less … Continue reading
In this Thinkibility Boost we will explore the relation between thinking and framing.
In visual arts and particularly cinematography, framing is the presentation of the visual element in an image, especially the placement of the subject in relation to other objects.
Framing can make an image more aesthetically pleasing and keep the viewer’s focus on the framed object(s).
Something similar happens with mass communication. In essence, framing theory suggests that how something is presented to the audience (called “the frame”) influences the choices people make about how to process that information. The basis of framing theory is that the media focuses attention on certain events and then places them within a field of meaning. Framing involves social construction of a social phenomenon – by mass media sources, political or social movements, political leaders, or other actors and organizations.
Framing is in many ways tied very closely to Agenda Setting theory. Both focus on how media draws the public’s eye to specific topics – in this way they set the agenda. But Framing takes this a step further in the way in which the news is presented creates a frame for that information.
Political Framing
This is usually a conscious choice by journalists – in this case, a frame refers to the way media as gatekeepers organize and present the ideas, events, and topics they cover.
Most of the time framing is a technique used by politicians or their advisers to favor a wished representation of the facts, usually when things went wrong.
Political Spinning
This is called spinning. The main objective is to lure the public into believing propaganda. A standard approach used in “spinning” is to reframe, reposition, or otherwise modify the perception of an issue or event, to reduce any negative impact it might have on public opinion.
Spinning might be discovered by misleading or false
What could we learn from solved cold cases? What has caused that the case is solved after years of investigations without results? What were the reasons that a solution was waiting for discovery, but never did? Solved cold cases are illustrative for how we think wrongly.
According to Wikipedia, a cold case is a crime or an accident that has not yet been fully solved and is not the subject of a recent criminal investigation, but for which new information could emerge from new witness testimony, re-examined archives, new or retained material evidence, as well as fresh activities of the suspect.
However, it is not always new and fresh information that helps to solve the case, but new perceptions. Even new information tends to be interpreted in old perceptions. Information is not perception.
Sometimes a viable suspect has been overlooked or simply ignored due to then-flimsy circumstantial evidence, the presence of a likelier suspect (who is later proven to be innocent), or a tendency of investigators to zoom in on someone else to the exclusion of other possibilities (which goes back to the likelier suspect angle)—known as “tunnel vision”
In our view, solved cold cases are excellent vehicles to study thinking methods, as what students and scientists are supposed to do at the Thinkibility University. At its East Wing they are excellent in Reverse Engineering of Thinking Strategies we wrote earlier about.
The people working there are not trained as criminal investigators but will use systematic and deliberate creative thinking techniques. Not only to improve forensic investigations but improving thinking methods in general. For scientific research as well for daily practical thinking.
As you might notice in the following mind map, little attention is given to the role of perception in this example of a Crime Scene Investigator Job Description:
The following books could be good starting points to be studied by the East Wing:
Solving cold cases happen when limiting thoughts and behaviors are challenged.
But how to challenge “limiting thoughts and behaviours”?
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There are several ways to design a new concept deliberately. But what is a new concept? Not many people have a quick answer to this question, other than “differently than expected”, “something else as normal”, “not seen before” or just “interesting”.
One of the easiest ways to design a new concept is to escape from an existing concept. For that, we need a concise description of the existing concept, otherwise we will be lost in all the details containing in the concept.
In an earlier blog post we gave some handles to describe a concept:
For instance:
Escape for instance the mechanism: instead of two there are three teams and three goals.
You can imagine that three-sided football will be a real game-changer, literally. As the BBC mentioned: a game of alliance and betrayal. The whole dynamic of the game is dramatically changed.
Once a new concept has”opened up” in your mind, we might “see” the world in a disruptive way, through the new concept and take analogies from it.
For instance: the conflict in Syria is basically a three-side game. That might open up new strategies and policies for military planning and diplomacy. Or prevent from blundering into military and diplomatic disasters by interpreting wrongly a three actors situation as a classical two actors cold war situation.
Or we can apply the same concept escape to another two-sided game, f.i. three player chess
We could also escape from the aspect of values, by excluding, adding, or changing values.
Escape from values: add spectacle to the game.
Ideas: change the game from 2D into 3D, using acrobats.
Actually, the concept change is from 2 D to 3D. This new concept can then be applied to other 2D games and opens new possibilities, like 3D chess
The same we could apply to escaping from function (goal, aim or objective of the concept). F.i. the function of a pension is that people who can’t work any longer are provided with a minimum of money to survive.
We could change the function: let’s suppose that the aim of “pension” is to keep the retiree as long as healthy. Now we can come up with mechanism that could realise this new function. For instance that the retiree does not receive a fix amount of money per year during the rest of his life time, but a linear increasing one. This would stimulate the retiree to stay active during the first years of his pension, by doing some paid work during some hours in the week.
Many new business models are the result of changing one or more aspects of the concept description. See for example how these fast-growing, innovative companies are redefining money lending, e-commerce, and more,
We invite you to describe how their innovative business models differ from classic business models in terms of function, mechanism and value.
Key Performance Indicators are meant to keep an organisation on track. By measuring the performance over time, you are able to look at deviations and to take measures. As Wikipedia defines it: A key performance indicator (KPI) is a type of performance measurement. An organization may use KPIs to evaluate its success, or to evaluate the success of a particular activity in which it is engaged. Sometimes success is defined in terms of making progress toward strategic goals, but often success is simply the repeated, periodic achievement of some level of operational goal (e.g. zero defects, 10/10 customer satisfaction, etc.).
The concept behind Key Performance Indicators is to build a feedback loop between input and output. Its working principle does not differ from a thermostat, which senses the temperature of a system so that the system’s temperature is maintained near a desired set-point.
In order to get not market driven organisations more efficient the adage “The numbers tell the tale”has become fashion among governments, institutions and not-for-profit companies. There are several metrics or key performance indicators.
However, Key Performance Indicators can also lead to perverse incentives and unintended consequences as a result of employees working to the specific measurements at the expense of the actual quality or value of their work. In the social sciences, unintended consequences (sometimes unanticipated consequences or unforeseen consequences) are outcomes that are not the ones intended by a purposeful action. Perverse incentives are a type of unintended consequence. A perverse incentive is an incentive that has an unintended and undesirable result which is contrary to the interests of the incentive makers.
There are a lot of examples of bad designed Key Performance Indicators. We came across, but not exhaustive:
Many Key Performance Indicators have unintended effects. They function as rules for behavior. Key performance Indicators are designed to notice need for adjustments of the course of an organisation. However, more often than not, they are invitations to cheat, by employees but equally by companies and institutions, especially when financial consequences are attached to the KPI.
Whenever designing or encountering a Key Performance Indication, be warned!
For more examples of perverse incentives, see here. For examples of unintended consequences see here.
To built up your Thinkibility skills, imagine your are the director of a hospice. You have set a thinking task: how to improve the occupancy (KPI) of the hospice. Then check your answers with How Dying Became A Multibillion-Dollar Industry.
Data collection is the process of gathering and measuring variables of interest, in an established systematic fashion that enables you to answer stated research questions, test hypotheses, and evaluate outcomes. The goal for all data collection is to capture quality evidence that then translates to rich data analysis and allows the building of a convincing and credible answers to questions that have been posed.
However, many research and policy are based on already available information that do not necessarily fit to the issue or question. For instance, the average income of residents in a district is estimated at the average market value of homes. Suppose there are some big villas in a district. However, the houses are rented to migrant workers who sleep with twelve persons in a room. Actually, the district has poor inhabitants and impoverishment is lurking. A better measure of prosperity in a district could be the amount of call shops, Travelex foreign exchange bureaus or percentage windows with bed sheets as curtains or without curtains at all.
Perhaps we could use data of the local supermarkets to assess the risk of impoverish of a district, not only by sales volume, but also the kind of products consumers buy. Income groups differ in the food they buy. Supermarkets may have more or less variety in products they offer, regarding the population.
This could lead to a Reverse Open Data movement, where businesses make their data available for town halls to design social and economic policies. Open Data is a movement that open data should enable third parties to leverage the potential of government data through the development of applications and services that address public and private demands. This information exchange could be made two-sided.
Perhaps we could measure the level of civilization of a nation by registering the percentage of cars that don’t stop for a pedestrian at a pedestrian crossing (a zebra crossing).
Another example is the Big Mac Index, a creative alternative to determine exchange rates, the rate at which one currency will be exchanged for another. It is based on the concept that a Big Mac is highly standardized all over the world. The difference in selling price in country A compared with the selling price of a Big Mac in country B gives a better idea of the value of the two currencies relative to each other.
Maybe we can say something about the tendency to conformity by counting people who wear clothes of a specific brand. Or determine the percentage women that is involved in street sweeping.
It’s hard to get solid data on drug usage, because it’s traditionally gathered via questionnaires. Respondents can fudge the answers or forget details. Drug users also sometimes don’t know what they are really taking or whether other drugs are mixed in. However, the laboratory analysis of waste water has the potential to get more accurate results more quickly, as a recent study showed that cities’ sewer water exposes use of cocaine, cannabis, meth and ecstasy.
It is interesting to roam in a town and create some mini theories that explain what is observed, compared with another town. For instance:
What does it mean? Or what does it explain? Can you quantify the phenomena and turn them into meaningful data?
Some time ago Robert Sheldrake suggested ten dogmas of modern science. In a TEDx presentation he argued that science, by using a rigorous method, has become a belief system that has become the default in the scientific community and this system is based on reductionism and mechanical philosophy. This idea caused an uproar in the scientific community and – ironically – also the watchers of the YouTube channel “Ideas Worth Spreading”.
As followers of Thinkibility already know, the more controversal a topic is, the more interesting. Sheldrake had apparently broken a pattern of thinking or challenged a common pattern and this was followed, as it always does, by anger and attack. Below are the ten assumptions Sheldrake suggested in his talk.
You may or may not agree these assumptions, but that is not the point to be taken here, in our point of view. We belief that putting these assumption in a True either/or Untrue box is a premature approach. Of course, we could use these assumptions as preliminary hypotheses to work with, yet it is wise to acknowledge your assumptions and not regarding them as dogmas. Instead of that we could better, or even must, regard them as possibilities and triggers to question: “What if?”.
Also, as Kant argued, it is not sufficient that hypothesis are positive confirmed by experiments. Kant recommended that we should indicate under which conditions a hypothesis would be wrong. A hypothesis is called falsifiable if it is possible to conceive an observation or an argument which proves the statement in question to be false. Could we?
Challenging the dominant thinking patterns in science could led to a paradigm shift as Thomas Kuhn described in his book The Structure of Scientific Revolutions. A paradigm shift is a change in the basic assumptions, or paradigms, within the ruling theory of science. It is in contrast to the idea of normal or generally accepted science.
Beside our considerations above, it would be an excellent exercise in Thinkibility to think up a compelling science fiction story by challenging the standard beliefs of science.
What if ?
We have written about standard patterns and possible escapes from them in the following blogposts:
