What is Artificial Whistle Blowing?
Gerhard Foerster
(Working Paper AWB16-01E short)
©CopyrightGerhardFoerster,2016,allrightsreserved
AWB16‐01EshortPage1
Summary
Economic theory in connection with econometric methods offers a huge amount of
models to explain, to forecast and to make rational decisions concerning almost all
economically relevant phenomenon of our today’s economics live. This objective
knowledge has the potential to act as artificial economic intelligence in form of artificial agents which can support the workability of out spontaneous order, the efficiency
of the economic policy and the efficiency of our political order. The present Working
Paper designs a sketch of artificial economic agents to reveal corruption und Securities Fraud on Top Management Level of public corporations which is well known in
the cases of Enron, WorldCom, Parmalat and Flowtex which became a wretched reality of a non-neoclassical economy. It is about Artificial Whistle Blower.
©CopyrightGerhardFoerster,2016,allrightsreserved
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Introduction
Artificial economic intelligence in form of Artificial Whistle Blower represents a specific kind of the application of artificial intelligence which is neither yet discussed scientifically nor popularly. While neoclassical economic theory assumes the perfectly informed non-opportunistic homo economicus today we know that this assumption is
far too restricted. The results are a) market inefficiencies resp. market failures on the
one hand and b) on the other hand conscious misinformation, opportunism, contract
inefficiencies, rule violations by fraud and corruption on all levels of the economy and
the society. The Invisible Hand which was meant by Adam Smith as a metaphor not
an explanation for the high efficiency of free markets gets off duty. The thesis is: Artificial economic intelligence in form of Artificial Whistle Blower offers a potentially
efficient instrument to support and increase the efficiency of the Invisible Hand.
The damages of corruption worldwide are extraordinary high. Whistle Blowers as
preconditions to reveal corruption are mostly inefficient. Artificial intelligence in form
of Artificial Whistle Blower can combat corruption without punishing a natural person
with high so-called exit-costs in case she is identified as a Whistle Blower. The thesis
is: Artificial intelligence can adopt the function of Whistle Blowing in case of corruption. Doing this artificial intelligence which is normally accused to destroy human jobs
can be used complementary and not substitutive to human capital in cases human
capital cannot be successful.
Methodological basics of artificial economic intelligence
Economics as an axiomatic theoretic and empiric science knows three different types
of models:
1. Explanation models: The objective of designing and using this type of models
is to explain empirical phenomenon in economics through economic causal
models. In this context the phenomenon which is to be explained empirical is
represented by the model-endogenous variable, while the causal reasons are
represented a) by model-exogenous variables in single equation models or b)
by model-exogenous and model-endogenous variables in models with multiple
interdependent equations. The models work in past times and not in future
times. The models can be static or dynamic.
2. Forecast models: Forecast models can be designed by making explanation
models dynamic if they are not yet dynamic and by shifting for one or more periods into the future. This can lead to a situation where model-exogenous variables have to be shifted into the future so far that their empiric values have to
be determined theoretically (scenarios), empirically (experiences) or politically
(decisions by economic and/or social politics) because the empiric values of
these variables in the future are not yet known. Forecast models are mostly
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used as basic models to find out whether there is a need for actions by economic policy.
3. Decision models: These models serve for decision making in economic policy.
They are designed by defining the inversion of a forecast model. The endogenous variable which has to be forecasted by the forecast model is transformed into the desired exogenous objective-variable in the decision process
of economic policy. The models give the possibility to find out the appropriate
instrument variables of economic policy which are the respective modelexogenous variables in the explanation models and the forecast models. In the
decision models these model-exogenous variables become modelendogenous variables.
The three-way division of economic models is used especially with econometric
models. But this division is valid and used concerning economic modeling in general
though not in the specific formal and mathematical way which is typical for econometric modeling. The three-way division of economic models is State of the Art and well
known in economics.
Another three-way division of economic models is based on the „Rational Expectation
Revolution“ (look therefore on Miller, ed. 1996). In this respect we talk on the one
side about the economist resp. the economic politician who designs and uses an
economic model to do his job and on the other side about the agent who is the representative economic subject or the homo economicus of the economic system which
is theoretically implemented in the respective economic model in one or all of the
above mentioned three forms.
Three kinds of the agent’s expectations can be differentiated:
a) The economist knows more about the relevant economic contexts than the
agent. This was the standard paradigm of economic theory until 1961 (Muth,
1961). The Phillips-curve prevails as a classic example of the model type.
b) Rational expectations were introduced in economic theory for the first time by
Muth (1961). The agent knows as much as the economist about the economic
contexts. This was the death-blow for the Phillips-curve and the idea to reduce
unemployment through expansive monetary policy. Monetarism was built on
the theory of rational expectations.
c) Declining the ‘game’ of „who knows what?“ leads to a third version. The agent
knows more than the economist. We talk in this respect about endogenous rational expectations (Grandmont, 1985).
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What does this tell us? Thesis:
The non-artificial economic theory persists in 3c. The respective decision
models limp with their left leg like the ‘rabbit’ behind the ‘hedgehog’ and are
therefore inferior. Artificial intelligence can reach a position comparable to 1b.
Based on 1b-models it seems possible using artificial economic intelligence
to reach a position comparable to 3a. Decision models of artificial economics
are therefore superior to models of traditional economics.
Whether this thesis is valid in the context of monetary policy, unemployment policy or
labor market policy should be left open. Concerning the question whether economic
theory and policy can support the fight against corruption the thesis has to be highly
agreed upon.
Why can the artificial economics perform better than the non-artificial economics?
What are the specific characteristics of artificial intelligence especially in economics?
The following cursory enumeration of relevant aspects of artificial intelligence is not
perfect and complete but should give obvious arguments why artificial economics can
achieve more than non-artificial economics.
1. Highly automatic collection of relevant data.
2. Highly automatic analysis of relevant data using methods of data mining and
text mining  Switching of stochastic economic model variables (economic
rules).
3. Automatic interpretation und inter-connection of switched stochastic economic
model variables respective economic rules via Bayes-networks.
4. Automatic creation of reports to connect with natural economic intelligence offering feed-back possibilities.
5. Continuous automatic process with fixed and if necessary very short report
cycles (i.e., more than once per day) and different warning levels.
6. Highly efficient technical transferability and duplication possibilities of economic intelligence.
7. Partial and flexible adaptability.
8. Extreme learning ability concerning speed, data volumes and extent of material to be learned.
9. High anonymity of artificial intelligence.
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10. Extreme high ability of multiple usage of learned economic rules in different
economic intelligence environments (modular concept, component principle).
11. Extreme high redundancy (broad circumstantial evidence) offers much broader
application areas compared to natural economic intelligence.
12. Using artificial economic intelligence cybernetic self learning economic systems are possible a) to optimize political instruments and b) to adapt rationale
expectations of agents according to model c.
The 12 statements can be reduce to the single statement that i.e., econometric models are artificial economic intelligence as well but that they are limited in their application in scientific and political contexts. Artificial economic intelligence as it is understood in this paper and discussed in connection with examples of corruption of top
management in public companies offers on the one hand the application of best economic theory and models without on the other hand being limited by econometric methods. This is a tremendous extension of the application and usage of the infinite
fund of economic theory and literature.
The example of a whistle blower concerning the Enron-corruption-type two important
findings can show.
-
Finding 1: Difference between natural Whistle Blower and artificial Whistle
Blower.
Finding 2: Difference between natural economic intelligence and artificial economic intelligence.
The following picture is the basis:
Public companies worldwide with potential corrupt top management, i.e., Enron.
Damaged entities worldwide through Top Management Fraud/corruption
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Each public company which is listed on the respective capital markets worldwide irrespective of language, country, culture, laws, governmental and societal organization and structure exhibits according to Jensen agency costs. A specific form of
agency costs is represented by top management fraud in connection with corruption
which we have seen with Enron, WorldCom and other companies. This form for
agency costs cause high damages and is potentially widespread worldwide.
What happens to a whistle blower in this kind of fraud and corruption? This shown in
the following graph:
Phase 1
Whistle Blower supplies insider information to those who are interested.
Phase 2
The damaged entities accuse the whistle blower for their damage.
Natural Insider‐Whistle Blower with specific concrete insider information concerning fraud and corruption. The Jensen performance paradoxes of integrity lead to the fact that the whistle blower is accused responsible for the damages created by the fraud and the respective
corruption which is revealed by him by the respective damaged entities. This leads to
the exorbitant high so-called exit costs of the natural whistle blowers which as a result explain why there are mostly so few or no natural whistle blowers in most fraud
and corruption cases in public companies and the different areas and levels of economy and society.
What happens in the case of an artificial whistle blower? This is shown in the following graph:
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Phase 1
Artificial Whistle Blower offers indicative information to those who are interested. Phase 2
The damaged entities accuse the management for the damages caused by fraud and corruption.
Artificial Whistle Blower
Top Management
The artificial whistle blower reveals fraud and corruption of the top management. The
damaged entities accuse the top management for the damage which is created by
fraud and corruption. The artificial whistle blower owns no exit costs like the natural
whistle blower. This proves that corruption can only be controlled efficiently by artificial whistle blower. So far finding 1.
Finding 2 relates to the efficiency of artificial economic intelligence compared to natural economic intelligence. Practical experiences with prototypes of artificial whistle
blowers assume that there exists a relation of few seconds for an analysis of a public
company through an artificial analyst compared to two weeks for a comparable analysis through a natural analyst. Based on the above mentioned assumption that an
artificial analyst can quite easily be transferred to other languages and markets
through the high transferability of the components of an artificial economic model it is
imaginable that all public companies globally can be controlled concerning top management fraud and/or corruption based on a single core-system of an artificial whistle
blower. This would be a venture which could not be arrived by natural economic intelligence in form of economists and analysts. How many economist and analysts would
be necessary?
The system of artificial economic intelligence consists of two types of components:
1) Artificial intelligence in form of data mining and text mining consists of methods
which can derive conclusions and inferences from quantitative data (quantitative values) and qualitative data (texts) which can be delivered in stochastic
model variables respective learned rules.
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2) Artificial intelligence from type Bayes-Network. These are stochastic methods
to draw conditional inferences from different stochastically independent empirical facts which are represented by the in 1) mentioned learned rules respective
stochastic model variables. The stochastic decision tables of the BayesNetwork execute a comparable function to the stochastic regression parameters of econometric models.
It is evident that econometric models can be continuously improved using better and
more actual data which can be expressed as a learning capability of econometric
models. Exactly the same is valid for models of artificial economic intelligence as it is
defined above using better and more actual data which improves their performance in
explanations, forecasts and decision making. It has to be done a step further to explain that artificial economic intelligence models immanent in their method have to
imply a learning process which puts these models in a position to learn continuously
and without an end and to improve itself without asking for help from natural economic intelligence as long as the quality of the model variables are consistent with the
objectives of the design of artificial economic intelligence models (significance of inferences. Artificial intelligence therefore becomes potentially more intelligent than natural intelligence which only has the duty to control the quality standards which are necessary and contingent in the respective methodology. Artificial intelligence not only
can things do faster but also superior compared to natural intelligence.
The design of an artificial economic intelligence model can only be constructed by
natural economic intelligence. Prerequisite is that the design of the artificial economic
intelligence model has integrity according to Jensen and that the model is applied
according to the proposed objectives and usage. In this case and only in this case
there is no danger that artificial economic intelligence models are misused and that
these models pull away from control by human entities intelligence.
An artificial whistle blower is obliged to deliver indications of highest quality concerning top management of public companies with respect of fraud and/or corruption like
Enron, WorldCom and others. The possible criminal, legal, capital market law and
other economic law consequences have to be drawn and only be drawn by natural
economic and legal intelligence.
Artificial intelligence as a principal-agent-model
The literature about artificial intelligence argues with principal-agent models. Artificial
agents succeeding an assignment own a high intelligence which is implemented in
artificial algorithms. The recipient of the artificial agent’s work is a natural principal or
an artificial principal which again is an artificial agent for another natural principal.
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This is shown in the following graph:
Job Loss Fear Fraud Executive Compensation Fraud Refinancing Cost Fraud Exit/Entry Fraud A
A
P
Benefit of Fraud A
Is Fraud an “Ultima Ratio?”
P
A
A
A
A P
Opportunities for Fraud P: Artificial Principal
A: Artificial Agent
Top Management Fraud
P
A
Cost of Fraud P
Suspicions for Fraud A
A
Criminal Involvement The artificial main-principal „Top Management Fraud“ sends 6 artificial agents to deliver the results of their working. These artificial agents again send their agents, i.e.,
„Benefit of Fraud“ with its 4 agents, which on their part have to deliver the results of
their work to their principal. To stay in this example, „Benefit of Fraud“ as an artificial
principal receives the working results of its 4 agents, consolidates these results and
as an artificial agent delivers the results of its work to its artificial main-principal „Top
Management Fraud”. The work flow goes like a cascade from agent to principal. This
structure knows so-called end-agents which are not seen in the above graph. The
end-agents are sent to work by their principals searching for relevant data and information in the digital universe. Each agent in the Artificial Whistle Blower works independently based on automatic algorithms which he has learned from natural intelligence based on „training“ with big data (i.e., econometric models). Basically artificial
agents can and should be designed to self-learn. This self-learning process has to be
controlled by natural intelligence to guarantee necessary quality standards and integrity of the Artificial Whistle Blower.
Top Management Defrauding Capital Markets
Shortly after millennium a fraud scandal in USA shakes the global capital markets.
The Enron fraud case was one of the biggest business crimes in history. This case
has 6 chapters (Windolf, undated):
1. Accounting manipulations

The management of Enron developed reciprocal Energy-Future-SwapDeals in cooperation with Merrill-Lynch to book immediately expected
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



profits in the profit-and loss-statement which Enron expected to get in
the future by creating specific energy companies which seemed to be
highly profitable based on the upcoming liberalization of the US-energy
markets.
Despite the objections and doubts of Arthur Andersen (AA), the auditor
of Enron, and of Merrill-Lynch, the investment bank of Enron, the deal
was made.
The question is: Why did the auditor and the investment bank agree to
this deal despite their objections and doubts? The answer could be:
The deal was implicitly or explicitly connected with promises for additional business contracts ( corruption). AA realized as Enron-auditor
a yearly turnover of 25 Mio. $. In addition AA had a yearly turnover of
27 Mio $ coming out of consultancy deals with Enron.
Achieving or beating the profit-objectives resp. the profit-forecasts led to
an enormously increased share price of Enron stocks. Enron‘s Top
Manager therefore got high Mio.$-Bonus. ( corruption)
This only was one part of the criminal accounting manipulations.
2. Criminal Special Purpose Vehicles




Specific parts of the balance sheet of Enron were transferred into Special Purpose Vehicles which were capitalized with Enron shares. These
„Special Purpose Vehicles” (SPE) were not forced to be consolidated
with the Enron-company leading to the effect that the equity capital
quote and the credit rating of Enron were no more be stressed.
For example: Enron owned share of Rhythms NetConnections (RhNC).
These shares experienced a huge value increase. This increased the
profit of Enron considerably. But it was expected that the RhNC-shares
were going to decrease considerably too. Enron wanted to avoid this
value destruction in its balance sheet. But no bank in the market was
ready to give Enron a deal to hedge against a RhNC-stock price decline.
Enron started a SPE which was capitalized with Enron-shares. This
SPE sold RhNC-put-options to Enron. Enron therefore hedged itself
against RhNC-stock price risks in its balance sheet.
There was opposition against this deal neither from AA nor from the
Enron-board.
3. Personal enrichments

The management of Enron realized a personal cash inflow of 130 Mio $
(Kenneth Lay) resp. 30 Mio $ (Andrew Fastow) through the respective
criminal actions.
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4. Corruption



Enron donated money to representatives and senators which inquire
the Enron-file.
Manager in Enron (Lay and Fastow) donated 1,8 Mio $ to the republican party in 1989 to 2001.
The energy industry of America spend in 2000 159 Mio. $ for political
lobbying.
5. Rise and fall of Enron: Corruption, conflicts of interest and criminal actions



Beginning in 1990 up to 2000 the market value of Enron increased up
to 60 bn. $ which means 70 times its earnings. The capital markets expected great deals from Enron in the future.
Enron was elected as „most innovative and most admired company“ in
the USA.
Within one year from August to December of 2001 the share price of
the Enron stocks declined from 809 $ to 70 Cent. Enron went bankrupt.
(Chapter 11).
6. Corruption of certified experts.



Arthur Andersen took bribes from Enron in form of lucrative consultancy
assignments (AA consulted Enron in the respective SPV-activities).
When the Enron-case was smashed and an broad inquiry started AA
destroyed important documents. The result was that AA as one of the
big five global auditors disappeared from the market.
Banks, regulators, internal and external audit, investment funds and investment banks were also involved whether as insiders or as beneficiaries.
To bring the story to the point: It was the classical picture of corruption together with
top management fraud which is as usual as the other classical picture of corruption
together with highly criminal acts. An Artificial Whistle Blower has to give alert if the
top management of a public company which is listed on different capital markets undertakes fraud connected with corruption. This alert must be given with a respective
probability.
A second point is characteristic for the Enron-case. It is the massive over-valuation of
the company’s stock price which is produced by the fraud and corruption actions of
the top management. In order to get an alert with a high validity the artificial whistle
blower has to indicate the probability of a high over-valuation of the company’s stock
price.
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The Artificial Whistle Blower of the Enron-Type fraud and corruption shows the following basic structure:
Corruption
1
Overvaluation
1
1
1
0
Enron‐case
Potential Enron‐case?
Very high
High
0
0
0
No suspicious factor
Rationale Bubble?
Medium
No
Alert?
To create the necessary stochastic alert-information artificial intelligent economic
agents concerning fraud and over-valuation have to be designed and implemented.
An Artificial Whistle Blower is needed.
The case of Enron has stimulated the economic theory and economic literature concerning agency costs of public companies in general and top management fraud in
public companies especially. This literature offers highly valuable economic intelligence which has to be translated into artificial economic intelligence to build on this
basis an Artificial Whistle Blower for Top Management-fraud and corruption.
Potential natural principals of Artificial Whistle Blowers
Who could be interested to own an artificial Whistle Blower? Concerning the type
„Top Management Securities Fraud of Enron“ for example the following principals
could be the perfect user of an Artificial Whistle Blower:

Tactical users within the scope of their innate business:
Investors in the capital markets
Regulators of securities markets
Regulators of stock exchanges
Public prosecutors
Law firms which are specialized in class actions against respective
companies.
o Auditor within their scope of „forensic auditing“
o Insurances within their scope of “executive insurance”
o
o
o
o
o
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
Strategic users within the scope of their competition strategy:
Companies against their main competitors
Auditors against their main competitors
Law firms against auditors
Law firms against their main competitors
Google as the worldwide largest and most important Artificial Whistle
Blower
o Silicon Valley start-ups as suppliers of Artificial Whistle Blower-Products
o
o
o
o
o
Consequences: The value creation of an Artificial Whistle Blower is quite high depending on the dimensions of fraud and corruption. According to the potential for
global usage of the tool and according to the low capability of easily copying the tool
the designing, the creating and the usage of an Artificial Whistle Blower can incorporate a high monopoly premium.
©CopyrightGerhardFoerster,2016,allrightsreserved
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