Informative Simulation is More Politics than Money

Kivy plots for a simulation of populations, moving trends, and financial flows.

This week, I wanted to explore a non-zero-sum, agent-based simulation with political platforms and town populations. Given a set of towns with unique political platforms and individuals with political preferences who vote and can move, do we ever see a situation where all individuals are happy?

This is a highly simplistic model that does not conclusively show that free markets are non-zero-sum. Also, I’m definitely not a economics major!
Travis

Hurry! My Time is Limited!

If you are in a hurry, here’s the break-down:

Metric choice matters when inspecting a market to find causation.
Money is a ‘grease’ for personal-value-based choice.
I still love simulations!!!

Metric Choice Matters in the Simulation

While building this simulation, I had the opportunity to install metric collection for a variety of components involved in town voting, market transactions, and population movement. This was required for some debugging to understand why things might not be going as expected. In some cases, it was because my intuition was off. Other times, it was a legitimate bug in my code.

There are definitely people left ‘unhappy’ in this model. Optimization occurs when everyone settles where they are happiest (or the least unhappy).

In this case, each person ends up in a place that for them isn’t worth leave, but they may still be unhappy with their town’s politics. In other words, if there were a town with politics which aligned better with their values, they would move but no such town exists.

Money is the Grease

For me, this project really solidified a different thought process regarding money. I have always seen money as something I must attain from a corporation in order to purchase goods for myself and my family. Building this model has shown me that there is another way of viewing money. Values come first and money is simply a grease to help you attain those values.

Watching this message unfold in the simulation, I realized that I sometimes lack true value. The space is filled with meaningless material purchases. Given a strong enough set of values, you can produce goods which others value. Then, you can’t help but attain money, which incidentally helps you pursue the principles you cherish most.

This thought introduces a question for me. What happens when your values tightly align with the values of the people around you? What can be achieved in such an environment?

I Still Love Writing Simulations

I will be honest…most of the programs I write are not contrived at the outset. In fact, I often almost quit working on them out of embarrassment that there isn’t a clear reason to build. The question, ‘What are you going to get from this?’ creeps in every time. However, after building it, I always have new ‘A-ha!’ moments that feel inherently meaningful. This time was no exception.

Enough of that though! Let’s dig into the specifics of this simulation!

Person/Town Breakdown

Person in the Simulation

Political Preference

In our model, a person has a political ‘base’ preference represented by a binary number. Each bit is a political issue that they either agree with (1) or don’t agree with (0). In addition to this ‘base’ preference, each person is assigned a random ‘Hardheadedness’ value. ‘Hardheadedness’ represents the total number of political preference bits that a person must match with a town’s political preference bits before they cross the happy-unhappy threshold. These ‘Hard Preference Bits’ are selected and set at random during initialization.

In the example above, you can see a person with a ‘Hardheadedness’ score of 3. You can also see their ‘Base Preference Bits,’ and the randomly-selected ‘Hard Preference Bits.’

Happiness Calculation

A person’s happiness is calculated by comparing the political climate (the town’s current political platform), bit by bit, to the person’s base political preference (‘Base Preference Bits’).

If one bit of the town’s political platform aligns with a person’s base preference bit, that 1:1 match adds one to their happiness score. On the other hand, if the town’s political platform bit does not align with the person’s base preference bit, it will only subtract from happiness if this same bit is True (1) for one of their ‘Hard Preference Bits.’ If ‘Hard Preference Bit’ is False (0) for this platform bit, the individual does not care about the mismatch. Let’s look at our example again:

Example of person and person's traits in simulation

Those ‘Hard Preference Bits’ are reflected here in the ‘Base Preference Bits’ as the underlined bits. The underlined bits are the only bits that can affect the happiness score in a negative way if they don’t align with their town’s political platform.

Happiness is capped at the total number of bits in the platform. In this case, a max happiness would be 8. If a person is basically happy, their happiness starts at the platform bit count. Otherwise, it starts at 0.

Deciding to move

A comparison between a person’s happiness and their randomly assigned happiness threshold decides whether they move or stay. The program randomly generates this value between 0.4 and 0.6. Next, the program compares a happiness decimal to this random threshold. The happiness decimal is the happiness score (a sum of 1-8, described in the “Happiness Calculation” section) divided by the number of total happiness bits (which is currently 8 total bits). This division produces a decimal between 0-1. If our happiness decimal is greater than the random threshold between 0.4 and 0.6, the person stays in their current town. Otherwise, if the decimal is less than the threshold, then the person wants to move.

Let’s say the person desires to move. Now they must visit all other towns to see which make them happiest. In some cases, even if they wish to move, they are still happiest where they are. So, they stay!

Remember, just because the person wants to move doesn’t mean they can. A secondary barrier becomes important: Money! There is a cost to moving. A person can only move if they can pay the price. We will explore this later in the explanation of the Town object.

Town in the Simulation

High-Level

A town is responsible for:

Conducting votes on the current political platform.
Tracking the population’s seniority hierarchy.
Keeping the town bank, taxing, and paying people.

Conducting Votes

When the program conducts a vote, it iterates all persons in the towns population to add or subtract a value for each bit in the political platform (see the explanation of a person’s political preference in the sections entitled “Political Preference” and “Happiness Calculation”). The majority vote count result for a political platform bit (positive or negative) sets the same bit in the town’s platform to 0 or 1.

Tracking Seniority

At instantiation, the original persons in a population of a town receive randomly assigned seniority values between 0 and 1. This value represents the amount of time a person has spent in a specific town. For each step of the world, a person’s seniority factors into their wealth. As time passes, the seniority value increases slowly.

Keeping the bank

A town starts off with a specific amount of money in its bank. The bank slowly distributes money to its population based on seniority. A town also uses taxes and utility costs to keep money liquid and cyclic.

The program calculates taxes by multiplying a person’s annual income times the town’s income tax rate (a flat tax). It likewise calculates utility costs by finding the product of the average wealth of the town and a flat tax rate, then by multiplying this product by the individual’s seniority score. The idea is that if you lived here longer, you should pay more. Perhaps I should invert this idea…I’m still not quite sure (let me know your thoughts in the comments below if you think this is fair/unfair!).

The World

The ‘world’ is at the highest level. It is the overarching scheduler for towns and people. Its two chief responsibilities are:

Managing the market for moving (including money transfers).
Directing town voting cadence.

Managing a Market in the Simulation

A supply- and demand-model tracks the market for moving. The calculation for demand takes the number of people who want to move to the town and subtracts that value from the number of people who want to move out. The program divides this result by the total number of people moving to obtain a ‘cost adjustment’ value from a ‘base moving cost.’ This model considers supply infinite, as the towns do not have a population cap.

Voting Cadence

Every town votes at the same periodic time. The program checks this against each step of the world. The newly voted-in platform may cause some persons to move. This new paradigm may also make people who just moved into a new town unhappy.

Dynamic Interface Orientation

Plot 1 – Each town’s current population
Plot 2 – Total persons moved at each time step
Plot 3 – Total persons desiring to move
Plot 4 – Total wealth of each town
Plot 5 – Current cost to move to each town (Note: 50 = the ‘floor’ to this cost)

Observations

Here, there are two towns that are valued higher than the other towns at the start. In other words, more people want to move in than want to move out.
When people start moving, the populations of their former towns drop and the population of their new town increases.
Here, wealth is tightly coupled with incoming populations as they bring money with them.
Finally, there is a steady decline of people wanting to move when they can either afford to move or demand falls to a level where the cost is affordable.

Conclusion

It was super fun to write this simulation! This exercise developed into something more than just a technical exercise. I learned some economic and behavioral concepts regarding personal values and monetary impact.

I hope you enjoyed this week’s post. Thank you for reading it! Feel free to leave a comment correcting me. As mentioned earlier, I am not an economist :). If you want to see the code that drove this post, you can find it here on my GitHub (warning, it’s a bit messy).

If you’d like to check out some of my previous work on simulations, then please go to these posts:

To check out the Python GUI platform I used for the plots in this article, please check out Kivy here.

Merry Christmas and Happy New Year!