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If companies were set up as non-profits with eRepublik, the expectation would be that there would be consistency between quality levels in pricing.

For example: If Grain was set at $1.34 (including wages) per “Q” level, then Food would add the cost in wages per unit for the conversion per ”Q” level and there would be very tightly controlled pricing, ergo:

$1.34 (1 Q1 Grain plus production wage) + $1.00 (conversion wage) = $2.34 Q1 Food
$2.68 (2 Q1 Grain plus production wage) + $2.00 (conversion wage) = $4.68 Q2 Food (2.34*2)
$4.02 (3 Q1 Grain plus production wage) + $3.00 (conversion wage) = $7.02 Q3 Food (2.34*3)
$5.36 (4 Q1 Grain plus production wage) + $4.00 (conversion wage) = $9.36 Q4 Food (2.34*4)
$6.70 (5 Q1 Grain plus production wage) + $5.00 (conversion wage) = $11.70 Q5 Food (2.34*5)

However, such is not the case, because of the multiple variables involved and the profit incentive as part of the game. For example, let us say that the Q1 grain company employs an individual with a skill level 5 in Land and the Q3 Food Company employs an individual with a skill level 5 in manufacturing.

Now the Grain employee is producing 4.25 units of Q1 Grain each day, very deserving of a higher wage and the Food employee is producing 3.3 units of Q3 Food each day, again very deserving of a higher wage.

Additionally, the cost of starting, or upgrading a company is non-linear as well. For example, in simplest terms the Q1 Company begins with a cost of 20 Gold if purchased new, while in a truly linear economy the Q3 Food Company would cost 60 Gold (20 * 3) the actual cost to get a food company to Q3 is 90 Gold.

Company Quality Upgrade Table (Accrued cost in USD, Monetary Market exchange rate from Gold current 0.016/1)

Start Q1 Company = 20 Gold ($1,250.00)
Upgrade from Q1 to Q2 = 20 Gold ($2,500.00)
Upgrade from Q2 to Q3 = 50 Gold ($5,625.00)
Upgrade from Q3 to Q4 = 100 Gold ($11,875.00)
Upgrade from Q4 to Q5 = 200 Gold ($24,375.00)

Therefore, the Q1 Grain Company will need to increase the price per unit to cover the $1250 company start and rather than increasing the price of a linear $3750 for the Q3 Food company, the increase in price per unit for the Q3 Food Company must be adjusted to cover the outlay of $5625.

And that all happens prior to any profit incentive and excluding taxation of any kind.

Whew…starting to get the picture? Add to that wages that generally are set to both compensate a worker for their skill level and the wellness cost for working at a higher quality level company…and as I wrote before things start getting hairy.

So the next question becomes, how in the world do you analyze economic trends and analyze the impact of tax policy on such a sophisticated model (and how in the heck does the pricing of 5 Raw Materials and 5 Consumer Products become so twisted?)?

That is where basic economic theory comes into play. Since there is such a limited amount of archived information from which to pull information, and such a limited market, the process of analyzing the impacts of events and policies requires a greater amount of generalized consumer price monitoring.

Congratulations, if you have made is this far I am going to provide you with some information that you can use at your next social gathering to amaze your friends, baffle your enemies, and basically appear as smart as you already know that you are.

Standard Deviation

As an RL consumer, when you go to your local grocery to shop, you may go up and down the aisles looking for the best prices on a product. Let’s say that you are scanning the shelves for your best pick of Salsa for the chips you just picked up. On the shelves you look at the various 12 ounce jars and cans and find that there are 5 products, all with differing prices:

Salsa A: $2.75
Salsa B: $4.00
Salsa C: $1.80
Salsa 😨 $7.50
Salsa E: $3.25

Now the average price for these 5 items is $3.86, therefore is might be surmised that anything above that is a bad deal and anything below that is a good deal. But since you do not have a calculator with you to determine the average, you instead make the determination based on the expected range, that is where standard deviation comes in. I will not bore you with the formula, but suffice is to say that the standard deviation on our salsa choices is $2.19, therefore it is your expectation as a consumer that the Salsa price range (this is very broad due to the limited sample) is $1.67 ($3.86-$2.19) and $6.05 ($3.86+$2.19). Which means that all of the jars of Salsa, except Salsa D, fall into an expected range.

“But what about Salsa D?” You might ask. Well, this is where you get to have some real fun with economics, the price on Salsa D is an outlier, which means that is lies outside the control band (or, in this case, the expected price range) of the Salsa options. So, you pick up the salsa, read the label, and determine that the reason (outlier cause) that the Salsa is so much more expensive is that it is fresher and contains clearly superior ingredients to the other options available (and certainly a quantum leap in quality to that canned tomato paste with a twist of jalapeno choice, Salsa C).

Enough of Salsa shopping, let’s get back to how to use the magic of standard deviation in eRep economics, where the process is simplified because all of the products are exactly the same in quality. Not that I do not use Q level, but simply head-to-head quality, wherein Q1 Food Company A’s product is exactly the same as Q1 Food Company B’s product.

I obtained the following minimum average prices over the course of an 8 day time span on the eUS markets:

Q1 Iron

442 -$1.26
443-$1.14
444-$0.89
445-$0.76
446-$0.99
447-$0.91
448-$0.93
449-$0.91
450-$1.42

The average price for the period for Q1 Iron is $1.02 the standard deviation is +/- $0.21, therefore the expected range of pricing domestically would be $0.81 to $1.23.

Now, let’s look at the product to which Iron is converted :

Q1 Weapons

442 -$7.72
443-$7.64
444-$7.24
445-$7.27
446-$7.69
447-$7.33
448-$7.44
449-$7.32
450-$10.26

The average price for the period for Q1 Weapons is $7.77 the standard deviation is +/- $0.95, therefore the expected range of pricing domestically would be $6.81 to $8.72.

So what can we gain from this information? Well first off, with even this small sample, we can determine that Resource Materials are far more volatile in pricing than it’s related manufactured product. Note that the standard expected deviation in pricing when identified as a percentage of the average units market price for Q1 Iron is approximately 42%, whereas the same percentage for Weapons is approximately 25% (I might add that Iron and Weapons are two of the most volatile markets anyway). But we will speak more to volatility in the next installment when we discuss the impact of taxes on the market.

But for now, lets analyze this small sample of market prices to determine what is tells us. Well, while Iron is all over the board in many respects there are three outliers, two spikes on days 442 and 450 and one dip on day 476. Now, when we compare that with Irons related manufactured product, Weapons, we find that there is only one corresponding outlier, a spike on day 450.

Now it does not take a seasoned economist to determine the cause for the corresponding outliers on Day 450, that is the day that the eUS went into battle with Portugal. When presented with the probable causality of increased demand during the battle we can see the benefit of using outliers to the control band (statistically expected price range) to determine the effect of market events. And with enough data, we can begin to accurately determine what the probable impact will be on market prices of certain events. Is might also be noted that by watching the markets during recurring events such as Country Presidential elections, Congressional Elections and even Party Presidential elections we may be able to more accurately determine the impact on the market for the purpose of determining the best opportunities for purchasing (as a consumer) or selling (as a company).

The additional domestic benefit of knowing how to calculate standard deviation and obtaining large amounts of data for accurate forecasting also reaped by businesses is in the determination of business plans and successful pricing, because as all businesses should know, the lower you price within the control band, balanced with profit goals, the more likely you are to be successful.

With the next installment we go international to begin discussing the measurable impacts of Income, Import and Value Added taxes on markets for accurate forecasting and planning.