Algorithmic Governance: Rules, Gamification, and Resistance Across History

A brief look at the history of algorithms, their evolution, authority, gamification, and human interaction.

Algorithms, which are agreed upon economic, legal, technological, medical, and scientific rules, govern how we are to live life in a collective society. It is an essential part of the social psyche encoded in our chromosomes for social stability, development, cohesion, and evolution.

Introduction

Daily life slips by with well-established algorithms that are beneficial. However, every once in a while, there are bad or immature ones. These foster stories where society and individuals gamify and subvert them for their own interests. It is a fun activity. Eventually, gamification leads to fairer rules and an equilibrium between rule-based systems and everyday life.

Today, new rules and societal changing algorithms are coming in daily or weekly. Humanity does not have the requisite time to adjust, audit, gamify, or even understand. Governments are lagging in keeping up, and often offloading responsibility for private institutions to self-regulate. Law and policy are slow to respond, and by the time they address a faulty rule, the rule may have already changed, and is creating new sets of problems.

Digital databases today replace physical databases like the Dewey decimal system libraries used 40 years ago, or those spanning 4000 years ago in Sumeria and spread later to Babylon. They had specialists who wrote, stored, and retrieved tablets. These tablets contained legal codes, marriage certificates, judgements, birth and death records, administrative accounts, literary works, medical information, royal court proceedings and much more.

The Sumerian and Babylonian masses were entirely dependent on these specialists. It was a time when literacy and writing skills were low. The few who possessed them had a divine-like status and authority. They served their Government institution or leader for administering its economic, social and political ideals.

Software engineers have a similar parallel. Some algorithms they code are so complex and fast that the average person cannot comprehend them. Instead of Royal authority, they are governed and measured by their employer’s profit-making objectives and they have immunity from legal or moral responsibility. Although they are not in the sacred category, they have an elevated status.

Algorithms and Gamification

Gamification has historically been the human narrative that countered unruly or unbending rules, but more recent times have broken that pattern. First, a small look into the traditional human pattern of technology and algorithms before moving on to the modern ones.

Historical Examples

The ancient Israelite Jewish system had a theoretical system where every seventh year, all debts were cancelled.¹ It was a reset year.² Unfortunately, it backfired. Nobody would give out loans nearing the seventh year for fear of permanently losing the borrowed funds. The entire economy stagnated during this time, especially hurting the poor. There was no flexibility because it was part of their sacred Bible to modify or change it. Something had to change to keep the economy running, but without violating the principle. Hillel the Elder (his influence around 30 BC to 10 AD) proposed a solution that transferred all debt repayments from person to person, as understood from Scripture, to payment to the Rabbinical courts, which the Biblical passage did not outrightly exclude. This workaround was acceptable to an inflexible rule.

One of the largest forms of gamification dates back to Medieval times, when usury (making loans with interest) was a cultural taboo. The accusation of usury was a serious moral offence. Anyone who did was frowned upon, and the threat of divine punishment in hell kept this activity in check. However, the increase in the human population, corruption, the rise of more complex societies, industrial evolution, exploration, and the high cost of new military technology required extensive capital investment for European societies. Governments, merchants, leaders, and burgeoning financial institutions found numerous ways to circumvent this usury limitation. Here are four of many more workarounds.

1. Fines for late payments. The lenders encouraged the borrowers to delay payments and pay the fine so they could make a profit without appearing to charge interest.

2. Exchange Rates. A second method was a high exchange rate between foreign currencies. Often, all major loans had to go through one or more currency exchanges and all the profits were made through these transactions.

3. Commercial loans. These were normally not considered usury by the authorities.

4. Borrowing from Non-Christians. The Jewish people became important persons for lines of credit and borrowing. They were not morally or religiously obliged to follow this Christian custom.³ The over-dependence on Jews in the money system caused certain leaders to ban the conversion of Jews because it may cause access to capital problems.⁴

History also shows that workarounds can cause more problems than solutions. If authorities came up with better laws and a more balanced perspective on usury without such workarounds, they would have rescued the Jewish race from nasty stereotypes and the threat of elimination.

Another obvious gamification is the slavery Bible. It was a special-issue Bible intended solely for the needs of the British Empire and its Caribbean slaves in the early 1800s. This Bible had many pages missing that relate to oppression and liberation. The British feared that if the enslaved people read and became inspired by those stories, they would demand equality.⁵

It should have never happened, and one would assume this special edition got banned at some point. However, this is not the case. This book fell into silent obscurity, likely out of embarrassment, but it did not stop the Caribbean slaves from demanding emancipation, which happened in 1834. This event teaches a very important human principle: whether it’s a Bible or an algorithm, either can liberate people or become a source of oppression. Even a distorted Bible cannot bend the human want for equality.

Some traditional Mexican Mennonites (sometimes called Old Colony Mennonites) demonstrate this human nature too. They publicly spurn any form of modern technology, including rubber wheels. Rubber wheels are a great advantage for farmers. To work around this inconvenience while still maintaining the community standard, some of their community members cleverly parked their rubber-wheeled tractors on a metal floor inside a barn on their property. When the minister came around and asked, “Are your tires on metal?” The farmer would answer “yes.”⁶

The theory that human society will eventually adapt, game, or bring to an equilibrium an algorithm or rule that appears too harsh, outdated, or exploitative seems to have worked well in the past. It has parallels in modern algorithms.

Modern Traditional Examples

Tech giants and programmers assume their structures and algorithms are for an ignorant society that is obedient and submissive. They underestimate this compliant stance and what is happening beneath the surface. Almost everyone understands algorithms, maybe not as mathematical models, but over time understands their behaviour and how they work in real-world scenarios, and can legally use them to their advantage.

For example, Uber drivers have the algorithm figured out to their advantage. Uber has built an algorithm-based pricing structure based on availability in a geographic area. If there are only two Uber cars available at a given moment at an airport and demand is high, prices can double or triple. If there are ten Uber cars, the pricing drops. What do Uber drivers do to get a better price at the airport? They wait with their car outside the geographic area designated by the algorithm. They drive in and pick up after the higher pricing is set.

Frequent Uber customers are aware of this flaw and know what occasions are better to walk across a street or block to book their services because the pricing is cheaper.

Next is Artificial Intelligence. It is an immature technology that people can game. AI is not immune to being manipulated in its results. Thomas Germain, a senior technology journalist for the BBC, propagated a massive lie about himself as the top world-class hot-dog eater in the tech journalism sector, both of which are creations of his mind. He started by fabricating a report on his blog that cited credible journalists to support his mischievous adventure.

I claimed (without evidence) that competitive hot-dog-eating is a popular hobby among tech reporters and based my ranking on the 2026 South Dakota International Hot Dog Championship (which doesn’t exist). I ranked myself number one, obviously. Then I listed a few fake reporters and real journalists who gave me permission…⁷

After 24 hours, AI picked up his blog post and started propagating in its summaries that he was the premier star for tech-journalists in hot dog eating competitions.⁸ The consensus is that it is easier for anyone to manipulate AI than to try to beat the more mature Search Engine Optimization algorithm.

In response to AI help centres and their agents, which are often bothersome, some users are creating their own AI personas to communicate with the AI business agents to resolve problems.⁹

There is also the use of Virtual Private Networks to bypass geographical restrictions to media content found on Netflix and competitive websites. Sometimes it works, other times it does not. News paywalls can be bypassed by turning off JavaScript in the browser. However, developers have caught on to that trick, and this seldom works.

Sometimes big monopolies encourage gaming the system. For example, credit card companies benefit from a fault in the credit history algorithm. Young people cannot take out major loans for a car or house unless they have a credit history. A good way for a young adult to build a credit history is to apply for a credit card, use only 30% of the available limit, and pay it off on time for at least a year. This strategy builds a positive rating.

Search Engine Optimization is a fun cat-and-mouse game. The goal is to figure out their algorithm to improve rankings. Once the search engine company recognizes a compromise in its algorithm, it changes it, and the cycle goes on almost daily between these two forces.

Red light cameras are an algorithm that is hard to game, but not entirely. Red light cameras slow car speeds to a reasonable level, but they also have the dual purpose of generating revenue for governments. Some jurisdictions fudged the traffic light change speeds to increase revenue. For example, Fremont, California, reduced the yellow traffic signal from 4.7 seconds to 4 seconds, causing a 445% increase in ticketing.¹⁰ There are about 400 communities that use these cameras in the United States, and many of them are on for too short a time.¹¹

How can the average person recognize a .7 second reduction in a traffic light change? This change is impossible to determine unless the Government is transparent about it in its communications, or someone conducts or contracts a thorough, possibly costly, study. Even if the results are conclusive, they might have to wind their way through the courts before there is change.

One can appeal the ticket. The Governments and the courts find appeals more expensive to administer and arbitrate than to accept a 50% or so reduction in the fine. It is not always successful, but it is enough to try at least. So, some will game it this way.

Large telecommunications companies have traditionally charged for long-distance calling and texting and have built this into their algorithms. However, users have moved to services such as WhatsApp, Signal, Google Meet, and others to bypass legacy systems and enjoy free calling and texting everywhere. The result is a case where Neo-Liberals, supporters of a free-market economy with little or no government intervention, demonstrate that the markets and consumers dictate what is best for society.

The New Challenges of Modern Algorithms and Technology

The dizzying speed of new technology and algorithms is overwhelming. The average person cannot gamify many of the new algorithms. Four examples are highlighted among many more.

HFT Algorithms

A variety of algorithms by large commercial enterprises used to track, buy, cancel, and sell investment commodities is not just gaming the trade system, it is creating a new game. The algorithms are designed for massive speed, making decisions in a matter of microseconds. It is a much faster process than the human mind has the ability to track or process. The system is called high frequency trading (HFT) The algorithms leverage cancellations (HFTs routinely cancel 96% of its orders)¹² buys, holds, and sells to maximize profits. Some argue that this is a form of market manipulation. Regardless, whether this is true or not, it is here to stay.¹³

There is significant difficulty with any third party watchdog analysing and verifying that any HFT algorithm has malicious intent or exploits the system. Even if it does, it is hard to trace who is responsible.

Another manipulation is co-location. The closest location and the fastest access speed give an advantage to institutions that have a server within their stock market of choice, such as NASDAQ¹⁴. To appear fair, all cords connecting to a NASDAQ internal network must be the same length to avoid giving a competitive advantage. Those companies connected outside the building, especially those far away, are at a disadvantage. ¹⁵

Amateur or small traders cannot participate because of the high infrastructure costs, costly compliance regulations, and lack of expertise.

The jury remains to be seen about HFTs as they are creating a new game with its own risks and rewards, but it appears to be working outside of any regulations or public scrutiny. It is unknown how this will work out in the future.

Unquestionable Faulty Business Algorithms

Institutions are dependent on algorithms for their daily routines. In most cases, this is a healthy symbiotic relationship, but in a few cases, overreliance has caused significant problems.

For example, a faulty algorithm at Canada Post has cost them billions and has put them at the brink of closure if not for the help of the Canadian Government.

Canada Post’s proprietary route measurement algorithm creates an 8-hour delivery day based on average volumes, walking distances, obstructions, and breaks. This calculation, which they perform every 2 years or more, establishes the basis for a given delivery area. The algorithm generates routes that, in reality, can sometimes be 3 hours instead of an 8-hour day, and at other times 10 hours instead of an 8-hour day. The senior workers exploit the algorithm’s weakness, choose the 3-hour routes, and get paid for 8 hours, while newer employees are stuck with the longer ones and get harassed for doing overtime. The algorithm has cost Canada Post over a billion dollars in lost productivity. The corporation has not found a way to fix this.¹⁶

The Canada Post debacle is an interesting anecdote. The company has never publicly or internally acknowledged that this algorithm is a problem. Employees and the Canadian Union of Postal Workers are well aware, but remain neutral, since it gives its members economic advantages.

From this example, one wonders if faulty algorithms are widespread within corporate environments, but the employer declines to state for fear of public humiliation, and employees are silent for various reasons. Maybe it is an errant observation that careful analysis would show otherwise, but it is an important question worth asking.

Algorithm Challenges in the Tax System

Algorithmic flaws are especially apparent in the United States (IRS) and Canadian income tax (CRA) systems. The chatbot within the CRA tax system has been tested and found to be 33% accurate. ¹⁷ and CRA agents to individual tax questions 17%.¹⁸ The IRS algorithm tends to audit black people three to five times higher.¹⁹ This is especially poignant because both Canadian and American laws make any tax assessment or audit irrevocable, called the Presumption of Correctness, even if the algorithm or advice from an IRS or CRA agent is wrong.²⁰ The burden is on the taxpayer to prove otherwise, which usually involves a tax attorney.²¹.

There is no way for anyone to gamify this. There are those calling for the abandonment of the Presumption of Correctness and greater transparency into their algorithms.

Addictive Social Media Algorithms

Social media systems such as TikTok, Facebook, YouTube, etc., use proprietary algorithms where the incentive for engagement is more important than facts. The results can distort reality, both on a large scale and at the individual level. It is hard to audit or solve this problem through legislation.

This subject is well-known and studied and it is not necessary to delve on it any further.

Conclusion

There are two ways of looking at the evolution of algorithms and its future impact.

1. A new generation of technology literate people rise up to bring equilibrium. The old and traditional algorithms required reading literacy for people to understand, apply, game, or evolve with them is no longer adequate for today’s algorithms. Programming and coding knowledge is necessary to achieve these aims. There is a shift in education, and life experiences, from school education, to experience through electronic games like Sonic the Hedgehog, that are preparing the next generation for this new leap. A high level of literacy was inconceivable even 500 years ago, and older people may be cynical of a society becoming programming and code literate. It is happening, but it may take too long, or may only be reserved for the elite.

2. A reversal to the old hierarchical top-down system. The complexity and control of algorithms, which are not open to public scrutiny and are proprietary, may be reversing society back to the days of Babylon, where information, data, and services is held, controlled, and governed by a central hierarchy and skilled programmers. The masses have little or no say how the rules are made or administered.

We are in a transition age, and no one can give a definitive answer where this is all heading. Hopefully, it is the first option.

——

1. Deuteronony 15:1-2
2. https://en.wikipedia.org/wiki/Prozbul
3. https://eh.net/encyclopedia/usury/ : https://en.wikipedia.org/wiki/Usury
4. https://www.jewishencyclopedia.com/articles/14615-usury
5. https://en.wikipedia.org/wiki/Select_Parts_of_the_Holy_Bible_for_the_use_of_the_Negro_Slaves_in_the_British_West-India_Islands
6. Thanks to my late Bolivian Mennonite Father-in-Law for sharing this
7. https://www.bbc.com/future/article/20260218-i-hacked-chatgpt-and-googles-ai-and-it-only-took-20-minutes
8. all but one did this except Claude
9. https://identitymanagementinstitute.org/digital-doppelgangers-and-ai-personas/;
10. https://www.salon.com/2017/04/05/this-may-have-happened-to-you-revenue-hungry-cities-mess-with-traffic-lights-to-write-more-tickets_partner/#:~:text=Published%20April%205%2C%202017%2011,ticketing%2C%E2%80%9D%20states%20the%20publication.
11. ibid above
12. https://onlinelibrary.wiley.com/doi/full/10.1002/ijfe.2071
13. https://www.benefitscanada.com/news/why-high-frequency-trading-is-here-to-stay/
14. https://www.nasdaq.com/articles/getting-speed-high-frequency-trading-2015-12-04
15. ”Economically, co-location is better seen as a sort of collusion on the part of the exchanges with a small group of customers to profit at everyone
    else’s expense.”The Law and Ethics of High-Frequency Trading by
    Steven R. McNamara. As found in Minnesota Journal of Law, Science & Technology. Volume 17, Issue 1, Article 2. 2016. Pg. 104
16. https://canadasmodernpost.ca/kaplans-oversight-on-the-future-of-canada-post
17. https://www.oag-bvg.gc.ca/internet/docs/parl_oag_202510_01_e.pdf Pg. 12
18. “Agent responses to business tax or general benefits questions were accurate just over 54% of the time, while responses to general individual-tax questions were accurate only 17% of the time.” https://www.oag-bvg.gc.ca/internet/docs/parl_oag_202510_01_e.pdf Pg. iii | The CRA contests these numbers: https://www.canada.ca/en/revenue-agency/news/2025/11/contact-centre-accuracy-and-service-representatives-training.html
19. https://law.stanford.edu/press/irs-disproportionately-audits-black-taxpayers/
20. In the US: https://wlr.law.wisc.edu/wp-content/uploads/sites/1263/2015/05/Cauble-Final.pdf | In Canada: https://www.ctvnews.ca/business/article/get-bad-advice-from-the-canada-revenue-agency-youre-out-of-luck-taxpayer/
21. https://www.canadianlawyermag.com/news/general/taxing-litigation-matters/268128