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Associative Remote Viewing (ARV) can be used to get an edge in the market, but it’s not a magic bullet or a get rich quick scheme. In this article I’ll outline the financial maths that I’m using with Second House Remote Viewing Group to squeeze the most edge out of ARV while maximising our profit over time.

The Setup #

ARV doesn’t work without an existing trading/betting strategy. It’s a tool you can use to increase your % of winning trades, but it’s also going to increase the number of trades you don’t take. So this article assumes the following:

you’ve got a system for finding trades/bets to make
you have a figure for the probability you’ll win
you know how much profit you’ll make if you do win
you know how much you’ll lose if you… lose

Without ARV #

Let’s keep it simple and look at a sports bet. This could be any kind of bet, so long as it’s a single binary choice: win or lose, and nothing in between. The probability $ p $ of winning is 0.7 (i.e. 70%) and the payout $ x $ is 1.4 (so if we bet $100, we’ll get $140 back with a net profit of $40).

graph LR; A["Bet $100 p=0.7 x=1.4"]-->B["Win, gain $40 p=0.7"]; A-->C["Lose, lose $100 p=0.3"];

With ARV #

We use ARV as a filter. We find a bet we might like to take, then we run an ARV target to attempt to test whether we’ll win or not. The bet has a probability of winning, the ARV has a nominal probability of being correct. We’ll assume that’s 60% for this example, but it’s variable. ARV will tell us “bet” (✅) or “don’t bet” (⛔)¹. Now our process looks like this:

graph LR; A["p=0.7 Bet"]; A-->B["Correct ARV ✅ p=0.6"]-->W; A-->C["Incorrect ARV ✅ p=0.4"]-->L; A-->D["Correct ARV ⛔ p=0.6"]-->P; A-->E["Incorrect ARV ⛔ p=0.4"]-->Q; P["Avoid $100 loss p=0.18"]; Q["Avoid $40 gain p=0.28"]; W["Win, gain $40 p=0.42"]; L["Lose, lose $100 p=0.12"];

As you can see, this changes the game:

probability of gaining $40 has dropped from 70% to 42%
probability of losing $100 has dropped from 30% to 12%
probability of doing nothing has risen from 0% to 46%

For the bets we actually make:

78% wins (70% without ARV)
22% losses (30% without ARV)

So this is our edge. Great - now what?

How Does This Affect Our Strategy? #

One striking thing about the above example is that we went from taking 100% of bets without ARV to only 54% of bets with ARV. We’ll look at the impact of passes later, but for now we’ll optimise the performance of those bets which we do take.

To understand a problem, it’s often best to start with the edge cases:

If we take a bet with a 100% chance of winning, ARV can’t help us do any better!
If we take a bet with a 0% chance of winning, ARV can’t help us either!
Between those extremes, ARV can help us.

But which bets are best for us to take in order to maximise our that benefit (our edge)? This depends on how accurate our ARV is.

If our ARV is right only 55% of the time, we actually gain the most edge by taking bets with a 47% chance of winning. With ARV, 52% of the bets we take are winners. Five more percent, not bad.

If we can get our ARV accuracy up to 70% then we can take bigger risks - here our optimum is to take bets with a 40% chance of winning. After ARV, 61% of the bets we take win - 20 more percent - nice!

Screenshot of a spreadsheet showing the opimum risk to take given different levels of ARV accuracy

Calculating Optimum Risk #

I am old enough that when I was in school, teachers still warned us of the importance of learning mental arithmetic with “you won’t always have a calculator in your pocket you know!”. Ohh, how wrong they were. Now there was a brief window of maybe a couple of months around age 16 when I could still remember how to work out this formula, but thanks to AI there need never be another.

$ p $ is the optimum bet risk, $ a $ is the ARV hit rate. Click here to see gratuitous mathematics deriving the following:

100% credit to Grok for the following explication:

The filtering system is modeled as a binary classifier that predicts whether a bet will win (signal to bet) or lose (signal not to bet), with both sensitivity and specificity equal to $ p(\text{hit}) = a $ (assuming $ a > 0.5 $ for the system to provide positive edge).

The filtered win probability is:

$$p(\text{win})_\text{filtered} = f = \frac{a p}{a p + (1 - a)(1 - p)},$$

where $ p = p(\text{win})_\text{raw} $.

The edge is: $$e = f - p = \frac{a p}{a p + (1 - a)(1 - p)} - p.$$

To maximize $ e $ with respect to $ p $, compute the derivative $ de/dp $ and set it to zero. This yields the quadratic equation: $$(2a - 1) p^2 + 2(1 - a) p - (1 - a) = 0.$$

Solve using the quadratic formula: $$p = \frac{ -2(1 - a) \pm \sqrt{ [2(1 - a)]^2 - 4(2a - 1)(- (1 - a)) } }{ 2(2a - 1) }.$$

The discriminant simplifies to $ 4 a (1 - a) $, so: $$p = \frac{ -(1 - a) \pm \sqrt{ a (1 - a) } }{ 2a - 1 }.$$

The root that lies in $ (0, 1) $ and corresponds to the maximum is the one with the $ + $ sign:

$$p = \frac{ -(1 - a) + \sqrt{ a (1 - a) } }{ 2a - 1 }.$$

So that means what, exactly? #

Let’s look at this on a chart.

Find your ARV accuracy on the x axis. For Second House RV Group right now that’s about 63%. The solid blue line tells you the win-probability of a bet to take which will maximise your edge over the market. The dotted blue line shows what your probability of winning that bet will be once you filter using ARV at the given accuracy.

As ARV accuracy increases:

the two lines get further apart (meaning we get more edge)
the solid blue line slopes down (meaning it’s optimal for us to take slightly more risk and hence hopefully get large returns)
the dotted blue line slopes up (meaning we’ve got an overall better chance of winning, even though we’re taking riskier bets)

Great!

Chart showing the relationship between ARV accuracy, the optimum risk of a bet to take and the outcomes associated with those bets.

While both betting and ARV are binary pursuits with no room for shades of grey, alas upon combining them we cannot help but notice the rather wide “grey area” across this middle of the chart. These are the bets we do not take. The darker grey at the top shows the bets we would have won if we’d taken them and the lighter grey at the bottom shows the losing bets which ARV has saved us from making. This can be a bit of a problem…

Cost of Passes #

ARV seems simple at first glance, but getting it right is hard. Maintaining accuracy over time is hard. Setting up the targets really well is hard. The whole thing is a tightrope walk. The upshot of this is that it’s hard on the viewers - the human beings doing this stuff behind the numbers - and if you’re not careful it can take an energetic and emotional toll on them which crashes performance.

As such, Second House group limits viewers to a maximum of one ARV session per day. The whole team works each target, and while we usually have consensus, a handful of % of the time we are unable to make a prediction.

We can see from the above chart that more than 50% of the time we don’t place a bet. Either because the ARV didn’t produce a clear answer or - much more likely - because it did and that answer was a “don’t trade” signal.

Optimistically assuming the viewers are able to consistently do six targets per week, that means we’re going to be betting on average only three times per week.

So even though ARV makes a profitable strategy more profitable, it can also slow it down. There’s two times this doesn’t matter:

A group of Remote Viewers pools their resources and works to build capital at a relaxed pace.
Trading or betting where you were going slowly anyway.

If you’re betting/trading every day and are profitable, the edge provided by ARV may well be wiped out by slowing you down. In other words, it might be more profitable to be less profitable faster than to be more profitable slower. You need to take this into account.

Cost of ARV #

Because ARV is hard to do consistently well and a niche market, it’s not cheap. If you’ve got four viewers and a tasker/analyst/administrator on the team, that’s five people’s skilled labour, probably on an unforgiving schedule. It’ll set you back at least $500 a pop. And remember some of those ARV targets are going to tell you not to bet/trade. So you need to be be making thousands of dollars profit on each win for ARV to make sense as a turnkey service. If you are, and your activity fits in with the above in terms of risk and schedule, then for sure give me a call and we’ll talk.

Of course a group of viewers working for themselves just have their time and availability to worry about. If you’ve got the skills and the group works well together then it’s a fun way to make yourself a lot more profitable! If this is a skill you want to develop I’ll be running a five week Associative Remote Viewing Masterclass in association with IRVA starting April 2026, so obviously check that out!

If you made it this far then I hope this has helped demystify the economics of ARV as a tool to improve betting and trading. It works, you can do it, and I hope now you’ve got a better idea of whether it’s a good fit for your particular application.

There are other ways than ARV to play the markets with Remote Viewing. Ways which are much less effort and gentler on the viewers. This is playing the long game - an approach for investors - using RV to look at actual reality rather than just abstract probabilities and charts. This will be the topic of a future article.

Binary ARV is designed to choose between two discrete outcomes. Sometimes, the RV sessions aren’t good enough to pick an outcome, and we have to “pass”. If there’s time and the viewers are willing then you can just run the ARV target again with a different pair of images. Bit if there’s not time (or budget), then sometimes you’ve just got to accept that ARV hasn’t delivered a conclusion. For a skilled team this rare, but it occasionally happens. I’m not factoring it into calculations here to keep things a bit simpler and because passes will vary per team/project. ↩︎