icm2re logo. icm2:re (I Changed My Mind Reviewing Everything) is an 

ongoing web column  by Brunella Longo

This column deals with some aspects of change management processes experienced almost in any industry impacted by the digital revolution: how to select, create, gather, manage, interpret, share data and information either because of internal and usually incremental scope - such learning, educational and re-engineering processes - or because of external forces, like mergers and acquisitions, restructuring goals, new regulations or disruptive technologies.

The title - I Changed My Mind Reviewing Everything - is a tribute to authors and scientists from different disciplinary fields that have illuminated my understanding of intentional change and decision making processes during the last thirty years, explaining how we think - or how we think about the way we think. The logo is a bit of a divertissement, from the latin divertere that means turn in separate ways.

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Knowdging and visions of paradise

Language and rhetoric of behavioural economics. Part 2 of 3: Do you speak normal?

How to cite this article?
Longo, Brunella (2016). Knowdging and visions of paradise: language and rhetoric of behavioural economics. Part 2 of 3: Do you speak normal? icm2re [I Changed my Mind Reviewing Everything ISSN 2059-688X (Print)], 5.3 (March).

How to cite this article?
Longo, Brunella (2016). Knowdging and visions of paradise: language and rhetoric of behavioural economics. Part 2 of 3: Do you speak normal? icm2re [I Changed my Mind Reviewing Everything ISSN 2059-688X (Online)], 5.3 (March).

London, 4 July 2016 - Any business case that requires to change language, media, procedures or behaviours causes great material challenges to people. Beliefs, habits and traditions, together with power conflicts, add further obstacles to change in any field - from religion to finance.

In Thinking fast and slow Daniel Kahneman quotes excellent examples of unexpected resistance to change by finance experts to whom researchers had introduced historical statistical evidence: showing hard data would dramatically challenge experts’ and professionals’ beliefs in any field but is very often unlikely to be successful if there is not a demand for change and no explicit endorsement for collaborative, appreciative knowledge practices, simply because of a stubbon refusal to look into the evidence. Wrong forecasts in the energy sector, for instance, have been quoted as an excellent case study for this astonishing, irrational behaviour we call information avoidance.

What media, language and message combinations are more effective and for what purpose?
Can narrative stories, visual arts and other more holistic and creative forms of expression reach out better than the mere use of the best organised and visualised statistics?
Can we prevent Stuxnet-like cyberthreats in industrial plants just through compliance with technical standards?
Do women like infographics more than men?
Can we measure the effectiveness of video lessons for the purpose of learning physics?
Is it convenient to translate a 300 pages report in 5 minutes video on YouTube?
How do we learn new insight from data filtered and clustered from open sources through algorithms for which there is no reverse engineering possibility?

There are no canned answers to such complex questions. The arguments or directions we want to knowdge people through (and about) need to be delivered with the most effective combination of languages and media, at a particular point in time, for a specific audience.

I have always found appropriate to design contents and workflows picking up data wisely and consistently from multiple bodies of knowledge and good practices, more than from just one disciplinary domain, even when apparently that was not necessary.
Metacommunication about the same process of knowdging is an essential part of it and is enormously enriched and speeded up when there are multiple sources of data and multiple views to compare.
Comparing and contrasting similar solutions (or problems) existing in different sectors allows the recipients to see that a continuum of possible choices exist. This is, per se, a very good way to trigger or foster, nurture or facilitate collaborations among experts.

Language or languages?

Science and requirements engineering are two examples of professional practices that have dealt with such language questions since long. And yet with no definitive answer on what type of communication assures success - and with whom.

Nisbett’s idea of a crusade against uses and misuses of multiple regression analysis is a good warning point for everybody: it reminds us that any effort to produce and leverage scientific knowledge chasing new causal relationships or creative correlations requires careful consideration of the chosen language or languages, channels and formats well beyond the appearance of formal consistency and compliance with a certain standard or framework, so popular in the IT world.

Behavioural experiments have been indeed "hopeless misleading" (Nisbett, Mindware, 2015) in supporting investments and research decisions and in selecting and conveying information in a wide range of circumstances.

For instance, health policy makers made an exercise few years ago in Iceland, Canada, Ireland and Thailand, with the implementation of a policy known as the "tobacco displays ban": the authorities imposed a ban on tobacco displays in shops and supermarkets. Scientists - mostly from the medical profession - assumed in this occasion the existence of an "out of sight, out of mind" correlation. They validated the idea of the display ban policy with statistical analysis and findings from so called "recall and recognition studies". The tobacco display ban turned out appallingly ineffective, even counterproductive on young people and with a negative economic impact on shops’ revenues.

Disappointing results in spite of scientific approaches have been obtained also in the USA in the food and drinks sector: here the assumption was that calories labelling and other immediate, visual forms of conveying nutrition data and prices would be useful to prevent obesity and to proactively inform the public about the unhealthy consequences of eating junk food. Researchers concluded that nutritional data in restaurants and fast food shops can be totally hopeless when not completely counterproductive (with the promotion of the cheapest unhealthy menus among those dionysiac spirits attracted by the claim "how much you can eat for just 4.99").

Behavioural exercises in other innumerable fields have left researchers and policy makers with bitter surprises, being the results most of the times not less anecdotal and biased than the usual practices and routines they would like to improve or change. But perhaps the most grotesque recent example that behavioural insight can get it wrong came from the academic laboratory, where one expects that scientists do not fly by the seat of their pants: investigating the moral attitudes of students engaged in bargaining the life of a mouse for a payment, researchers concluded that there is no morality in markets. After such conclusion was smashed as entertaining nonsense (and the same results of the experiment interpreted in exactly the opposite way by other senior academics), the researchers resolved to start a new investigation examining the correlation between happiness and... skills.

Could researchers in the above examples have applied previous studies, guidelines, empirical evidence and consolidated best practices from other disciplines and sectors straightaway, without any need of new experiments? How to anchor behavioural economics practices to sound scientific methodologies, preventing commoditisation trends?

I have recently asked the question to an academic researcher in respect of an experiment he made on behalf of a local authority, the result of which confirmed basic principles of information management and human computer interaction consolidated through four decades of studies and good practices. But... they did not know that! The experiment was designed without any investigation into prior literature about the best ways to convey procedural texts throug hypertexts. Neither they thought to verify prior research available in the public domain about consumers’ behaviours and choices in respect of the subject.

The genuine desolation of the answer I received gives an idea of why horizontal innovations and collaborations among experts across sectors are so needed and so complicated: he simply replied he had to sell the idea that behavioural economic is useful to local authorities and that's it.

Is selling behavioural economics advice a good reason for ignoring others’ expertise and major sources of information about the same customers’ behaviours?

Few hours of desk research in a business library could surely enhance the reliability of any behavioural economics experiment.

Assurance principles and normalisation

It seems to me that as a matter of very basic and preliminary assurance and normalisation, any knowdging experiment should follow the principles identified by the European code of practice for socio-economic research: Upholding scientific standards, Compliance with the law, Avoidance of social and personal harm.

These three principles outline a perimeter in which multiple disciplines and specialist languages, statistics and operational research technics can be adopted, integrated, developed and elaborated safely, both methodologically and from a content creation and management perspective.

We are still very far from an Esperanto language that would, of course, be the magic global wizard for business, engineering and research communications, giving more consistency and reliability to the same behavioural insights. Such Esperanto is unlikely to ever become available but for a possible surrogate for it achieved through machine learning procedures in controlled environment. But if we agree to use multiple regression analysis and other research methodologies in a controlled, open and accountable way we can at least assure that knowdging strategies do not risk to end in major cultural clashes or economic disasters.