Mafia Risk Management Dynamics when Invading Protected and Unprotected Territories by Michael V. Blumeyer

The emergence of mafia gangs and their growth trends are not popularly studied in finance, but are interesting to careen economists and analyst to take notice as there are consistent patterns and trends that can be explained through algorithms.  The history of the mafia has a deep and dark history, going back to times in which the Sicilian Mafia, the Russian gangs, and even the relentless Japanese Samurai, who have evolved into the Japanese Yakuza gangs in the midst of unprotected and also protected territories.  The strategy and tactics that these mafia gangs use to force monopolies, apply scare tactics, and execute “Godfather offers” may differ in their approach, but have prodigious similarities in their growth trends of how these gangs develop to have an impact in their inhabited territories.  For the Japanese Yakuza, drug lords dictate how drugs will be exported and imported, while the Sicilian Mafia have had notorious success with bribing law officials into allowing and even helping them execute deals through a type of psychological leverage that is notoriously known in finance defined as a “Godfather offer.”

The article’s purpose is to not delve into the history about the specific facts and certain tactics that the mafia used that are irrelevant to discuss, but to research and identify certain economic situations and algorithms in which mafia coalition is most likely to occur.  In addition, we will examine economic situations in which the mafia will least likely take risk in protected and unprotected territories.  Keep in mind that whether or not a territory is protected or unprotected, is not valid if one were to apply a hasty generalization accompanied solely with a macro perspective in the behavior of these gangs.  Instead, it is better to completely understand the economic viewpoints from the mafia’s potential risk/reward position vs. the territories enforcements’ risk/reward position.

Let us assume that mafia gangs are present in a certain region.  An innocent citizen (business owner, entrepreneur, investor, etc.) has three options that he can decide and pursue.  The first option is that he can either buy additional enforcement (hire lawyer, officers, guards, etc.).  The second option is for the buyer or “defender” to not take action and hope that the law and his networks can protect and defend any type of coalitions or gang activity that could affect his business directly or indirectly.  The third and final option is for the buyer to move away from the territory, but for the sake of the article, let’s keep the discussion focused on this dilemma.  We will possibly explore shifts and changes in mafia activity later on.

After reading the research article, “From the Wild West to Godfather: Enforcement Market Structure,” I would like to explain the algorithm used from the authors James E. Anderson and Oriana Bandiera, from the National Bureau of Economic Research.  These authors deserve the credit with identifying the situational algorithm, but I would like to analyze it further by breaking it apart in simpler terms and examine how these formulas can congruently play a dynamic role in economies where strong and even growing enforcement actually can have a direct correlation with the emergence and growth for mafia invasion.

The Options of the Buyer (Defender)

Let us assume that the buyer, who is a business owner or non-member from the mafia, decides to buy enforcement to defend his assets.  We can label this decision as pi.  If the buyer decides not to purchase enforcement, let’s label this symbol as β.  In a situation in which buying “specialized enforcement” can be possible, then β < pi.  This is because it would not make political and financial sense for the buyer to buy only specialized enforcement.  The buyer would want to have any additional enforcement that is at the least price possible to pay.  In the perspective from the marginal buyer’s point of view, the equation would be (pi – β)V(α).  The V(α) is simply multiplied into the previous equation.

To create a simulation with our algorithm that we have established so far, let us assume that we have five marginal large buyers, whose distribution centers are headquartered in highly-diverse locations.  These owners are in transporting industry that distributes silver from one distribution channel to manufacturers.  Let’s also assume that the economy is flourishing: the financial markets are in growth trends and are breaking out of their 52-week highs, the mood of the economy is optimistic, and Fed rates remain relatively low to further entice traders along with the robust growth of institutional investors.  If five marginal buyers encountered predator invasion from a mafia invasion and four out of five marginal buyers wanted to tighten enforcements even more, the formula would be (pi -  β)4V(α).

Let’s now take the “Godfather’s” point of view.  The gang sees that four buyers have tightened security, but there is also an option to invade one of the five buyers who has not purchased the specialized enforcement.  They have two options.  The first option is to invade and exert their power over the buyer who does not have specialized enforcement.  This part of the formula would be 1 – β.  The other option that the mafia have is to take on additional risk and attack a buyer that has prepared themselves with higher-ranked enforcement, which would be stated as 1 – pi.  The results for each invasion depends on some probability, but we can be confident that the mafia’s chances to succeed in their invasion would be better if they attacked the 1 – β buyer.

One other part of the formula that we must take into account is including  λ.  This symbol, lambda, represents a percentage of predators that decide to aggressively take on the additional risk….

(Full article to be posted soon.  Still putting the algorithm together and researching coalitions stats in unprotected and protected territories, then identifying congruencies….)