There are not many game-theoretic models for the study of African corruption.

So I have been wondering whether, as part of an early effort of figuring out some foundations, corruption in these parts could be examined through the combined lens of: prospect theory and base error neglect; and whether that might yield any insight for something sorely needed: a more rigorous economic model for studying the phenomenon in these parts.

*Prospect Theory*

This tool is a useful alternative to the usual method of assuming that decision makers evaluate the gains and losses that may result from their decisions with equal scales.

Assume that the main decision makers whose choices of action and inaction might impact on the prosecution and deterrence of corruption are concerned about the following potential outcomes:

1. A prosecution should bolster the image of the government as competent and effective.

2. A prosecution should deter other party officials from corrupt acts and reduce the risks of embarrassment to the government.

3. A prosecution should endear the ruling party to swing voters who are responsive to signs of good government.

4. A failure to prosecute shall add a powerful negative campaign message to the arsenal of the Opposition.

5. A failure to prosecute shall embolden a wide range of lower level officials to behave in ways likely to embarrass the government.

6. A failure to prosecute shall alienate important foreign donors with the capability to influence global elite opinion.

In a classical ‘expected utility model’, one may perceive a symmetry across the potential outcomes and may conclude that the gains of actions outweigh the costs of action such as:

1. Alienation of the party factions of the prosecuted official.

2. A signal to lower level officials to acquire ‘insurance’ against prosecution for past conduct etc.

Symmetric cost-benefit may thus be perceived as the primary tool for weighing action in this classical model.

In a ‘prospect-theoretic model’ however this approach is considered suspect. The entire situation could be framed in the alternative, for instance, by first recognising that ‘prosecution’ could have two results: success and failure, and that the costs of failure might far outweigh the costs of inaction. They may include:

1. Accusations of collusion with the defence or of general incompetence, which may alienate swing voters.

2. Elevated impunity due to perceptions of weak capacity.

3. Perceptions of unfair persecution of the acquitted officials among party members etc.

This is a very crude treatment, but the essential point is that ‘losses aversion’ can be a stronger motivator than ultimate reward, and that decision-makers may amplify the impact of losses and downplay the reward of gains.

*Base Error Neglect*

Combining the ‘mental accounting’ results of the above method with base error analysis can be jarring but also interesting.

The crude and basic definition of this base error neglect idea is the tendency of observers served with two sets of facts to ignore the first set which constitutes the primary evidence base and rather to focus on the newer stream of information from the second set.

Consider the following stylised facts:

1. Of the more than ten thousand political appointees Ghana has employed in the 4th Republic, less than one hundred have actually been convicted of corruption by a Court of law or indicted by a commission of inquiry or government whitepaper.

2. The recent auditor general’s report into public agencies did not clear any agency of irregularities, and none of the Parliamentary reports on the irregularities have been acted upon.

3. Of the x<100 convictions and indictments for corruption, only about a fifth have been reversed by the courts.

4. The likely conviction rate for corruption in Ghana by a government with the political will is likely therefore to be in the region of 80%

Is the fallacy very obvious to you? It probably is, but that is because this framing is especially weak. Usually, if properly framed, a good understanding of Bayesian techniques is needed to tease out the fallacy, which is that by the 4th point, most people would have disregarded the baseline evidence stack in statement 1, which heavily constrains the other facts in the subsequent statements.

Nevertheless, the effect of base error fallacy in this example is a heightened expectation of a successful conviction thus aggressively raising the ‘fear of losing’ risk for the decision-maker contemplating whether or not to prosecute suspected corruption.

In this formulation of the model for analysing decision making factors associated with corruption prosecution, it can be shown, albeit very heuristically, that base error neglect reinforces prospect theory in making decisions to prosecute corruption very hard for political bosses.

GWU Economist, Tara Sinclair, led the publication of a report recently that showed that in the 12 major economies of the world that constitute the overwhelming bulk of global GDP – including the US, Russia, China, India and Germany – the average measure of unfilled jobs as a percentage of total job openings is about 12.2%.
This is considerably more significant than total unemployment in many of the world’s leading economies.
Of the unfilled jobs, five sectors alone constitute nearly 92% of total absolute openings.
The percentage measure of unfilled jobs in each of these five sectors is indicated below:
1 Computer and Mathematical 83.8%
2 Architecture and Engineering 81.5%
3 Management 76.0%
4 Healthcare Practitioners and Technical 76.0%
5 Business and Financial Operations 59.0%
A careful look at the sectors indicate a strong compatibility with Artificial Intelligence-assisted skills enhancement. That is to say that the melding of new AI-mediated virtual reality capabilities and AI-enabled expert systems shall generate ‘cognitive exoskeletons’ or ‘second brains’ for many workers who would ordinarily not be able to perform to an adequate level on these jobs.
Furthermore, business model pivoting and innovation remains constrained due to the poor compatibility of skills available in the marketplace with the opportunities promised by such structural innovations.
In a world where average skill potential and mid-level employee capabilities can be considerably enhanced by artificial intelligence, businesses shall have a much improved prospect of hiring sound labour at reasonable cost for long-stalled or sub-optimally executed new operations.
For instance, health technicians and nurses equipped with new imaging and diagnostic tools and easy access to smart case analysis shall begin to perform at levels approaching specialist ability. As also shall paralegals and mechanics in engineering domains. Cheap laboratory tests and examinations and legal consultations could become the norm heightening demand for these services and therefore opening new markets with positive labour hiring implications.
The coupling of mid-level AI systems with mid-level human-cognitive capabilities is the much ignored revolution at the cusp of ‘medium-term change horizons’.
Average workers boosted by average AI offer a more fascinating glimpse into the near and mid future than terminator-level or Jarvis-scale machines making all the key mid-level decisions in technical and managerial domains.
Properly so-evaluated, it becomes obvious that some of the policy recommendations premised on the inevitability of a large-scale loss of mid-level jobs due to an exaggerated near-horizon automation risk can be tempered with a more pragmatic vision of the jobs landscape in the next couple of decades.

The idea that the main impact of artificial intelligence shall be to eliminate mid-skilled workers is a silly example of estimations based on one tail of the curve.

In the beginning, yes, some mid-skilled workers will lose out. But over time AI’s main impact shall be to *extend* the capacity of average workers to compete with the elite outliers.

A 3-year radiography diplomate technician shall have VR tools that dramatically enhance his insights relative to consultant specialists. Hospitals will hire more of those and few top specialists to cut costs.

Same dynamic shall impact law and management consulting. What would not happen is a wholesale replacement of mid-skilled workers whose coordination roles and ‘priming’ tasks for advanced technology shall actually expand.

Some pseudo-formalisations of this hypothesis shall follow in due course.

It took a while but Noble Law Group has finally put out online the audiovisual material from the symposium they organised a while back on the interrelationships of law and policy, on one hand, and development and culture, on the other hand. Here is a video of a panel discussion held at the symposium:

On reflection, there is a point that could have been more colorfully made, which is that in Ghana we practice and exhibit ‘Fetish Law’.

The best way to explain myself is to use as an example a rule that was widely enforced across many of the so-called ‘top’ secondary schools in Ghana sometime back. Form One juniors were required to have on their person at all times, and to show on demand, a neatly, folded, white handkerchief.

The rule evidently was about hygiene and proper self-presentation (‘Christian dressing’). The curious thing though is that juniors were always punished for showing a used handkerchief (called a ‘dirty hanky’). Obviously, the rule was self-contradictory. Yet, prefects and seniors would insist on it. Apart from the ridiculous fact of this ‘good rule’ being reserved for juniors, it also demonstrated the ‘mimicry’ and ‘ritual display’ qualities of Ghanaian rule-making.

The law is to induce awe. It is to intimidate. It is to establish moral hierarchy. But it rarely aims to influence the *social operating system* on which the routine affairs of communal life are to be managed. Since *politics* excels where *exceptionalism* prevails (cue Carl Schmitt), it is not surprising that in Ghana we have two contentions at play: those who will judicialise all politics and those who will politicise all norms. They deserve each other.

We may be missing one essential aspect of why the Trump phenomenon continues to trend despite the ceaseless parade of gaffes by the most unconventional major presidential candidate the West has ever seen.

I just ran a basic digital engagement comparative assessment of the primary digital media assets of the two leading US Presidential candidates, and Trump leads Hillary by a wide stretch.

Here is probably what is happening: faced with such a strange, and befuddling, candidate, one who can’t seem to stay on script or act in anyway remotely like an actual presidential candidate, the outraged media decided not to treat him like one. It has matched every caustic jab from his mouth with denunciation and resorted to treating him like Pol Pot in a bling tuxedo, that is with a mixture of revulsion and comic derision.

The public, tired of the one-dimensional portrayal, has grown more intrigued and has been bypassing the media coverage in search of other perspectives and a more nuanced narrative. And the only place they can get it is the man’s own websites and social media accounts. There they have found drama, but they have also found variety. Whether this is a product of well-paid PR consultants or the reality of a more complex human being than the media coverage insists, people are engaging more and connecting directly with the candidate.

For example, on Twitter, positive engagement with Trump’s self-generated content is 3 to 4 times higher than Hillary’s. Consistently over the last 3 months.

It is clearly getting to a point where people might switch off from the mainstream coverage of the Trump campaign altogether. If that happens the value of positive endorsements and coverage of Hillary will start to drop dramatically because in a competitive electoral campaign, such endorsements are only valuable *in comparison*. Where people are unwilling to *participate* in the invitation to compare, then that whole approach to media influence in politics loses its very essence.

Therein may lie the reason for the media’s inability to significantly shift public mood in this campaign season in America.