Graphical Assumption Modeling

13 ozziegooen 03 January 2015 08:22PM

The Flaws of Fermi Estimates

Why don’t we use more Fermi estimates?[1] Many of us want to become more rational. We have lots of numbers we can think of and important variables to consider. There are a few reasons.

Fermi calculations get really messy. After a few variables introduced, they could quickly become difficult to imagine and outline a problem. Many people, especially those who were not used to writing academic papers, do not practice the skills of formalizing inputs and outputs. It can be tedious for those who do.

Fermi models typically do not include estimates of certainty. Certainty propagates. It creates bottlenecks. As a Fermi model grows, specific uncertain assumptions could underscore the result. Certainty estimates are typically not measured, and when they are they require formalization and significant calculation.

Fermi calculations are not fun to share. Most of them are pretty simple; they just involve multiplication and addition and 3–5 variables. However, in order to write them one must formalize them as few lines of math him or few long paragraphs which really should be math.

We propose the use of simple graphical models in order to represent estimates and Fermi models. We think these have the capacity to solve the issues mentioned above and make complex estimations more simple, more sharable, and more calculable. A formal and rigorous graphical model could not only improve on existing Fermi calculations, but it could also extend them to functions they have not yet been used for.

Multiplication

Let’s say we are trying to estimate the number of smiles per day in a park. A first attempt at this may be to guess the number of people in the park and to estimate the number of smiles on average per person in the park.

This is easy to calculate directly. 100 People x 10 smiles/(day * person) = 1000 smiles/day.

As a model, we can represent the variables as lines and the function as a box in between them. This fits nicely with similar diagramming standards. The function of multiplication acts as an object with inputs and outputs.

multiplication_1.png

Independent variables, or user selected variables, are shown in black, and dependent variables are shown in blue.

We can condense this diagram by moving the number of smiles per day per person into the multiplication block.

multiplication_2.png

Say we wanted to find the total smiles per year in the park. We can simply extend the model as follows.

multiplication_3.png

Addition

Perhaps we think that kids and adults have different rates of smiling and would like to separate our model accordingly. We estimate the number of kids in the park, the number of adults in the park, and their corresponding smiling estimates. Then we add them with a similar block as we used for multiplication.

addition_1.png

Uncertainty

If we have uncertainty estimates we can make them explicit. Estimates of certainty typically get left out of Fermi calculations, but become essential when making large models.

addition_1.png

It is not clear what the best way is to annotate an uncertainty interval. In this case, the intervals described are meant as 90% Gaussian confidence intervals, but these could vary. They do not have to be Gaussian-like intervals, but could be complex probability distributions. These may require graphical representations and additional software. However, for many estimations, even simple models of uncertainty would be advantageous.

Estimate Combination

If two people give two estimates for a number, they could be combined to find the resulting probability distribution.

combination_estimates.png

Uncertainty distributions are valuable for this. If two agents both state their uncertainty distributions, we can find a weighted average of their estimations with a calculated resulting uncertainty distribution.

Model Combination

We can combine models by combining their resulting estimates. So far we have shown two unique attempts at modeling the number of smiles in a park. They produced the same unit output, so they can be combined.

combination_models.png

Both of them still have predictive power, and a combination could produce a more accurate estimate than either alone. The model with greater certainty, in this case the adult/child split model, will have more influence in the final calculation, but it will still be moderated by it. Combining many properly calibrated models will always give a more accurate result.

Abstraction

Large sections can be combined into black boxes.[2] Black boxes can be used to summarize large models into simple objects with specified inputs and outputs. This means that one can work on a very large total model in small pieces and have it be manageable.

black boxing.png

Decision Making

Say we must decide between two options. One common way to do so is to estimate a value for each, and choose the one with a higher (or lower) value.

decision making.png

In this case we make a decision of which lemonade will sell better. We use a decision ‘block’, which could hold any arbitrary decision function. In this case, it simply outputs the value of the highest input value.

This can be useful if one can assume the use of the best option of alternatives. In a larger model, there may be many decisions determined by model. The outputs of these decisions could be used for later estimations or decisions.

Larger Models

These techniques can be combined to produce large and intricate models. As these increase in size they can become more valuable.

complex.png

In the model above, a person is attempting to find the best use of their time to produce money. There are several options to sell lemonade, and there’s also the opportunity to work overtime. The estimator makes an estimate for each and uses the model to understand them in relation to each other.

This larger model demonstrates the option of configuration in these models. The profit percentage of lemonade sales was expected to be similar for different kinds of lemonade in different locations. It could have instead been multiplied individually for each one, but it was simpler to move it after the decision block between them.

In this case it may have been reasonable to use a table instead of a graphical model. However, a table would not necessarily demonstrate the unique constraints and considerations of each type of input. For instance, lemonade sales had a margin of profit, and overtime work had a different net income number. In tables many of the important calculations are often difficult to read at the same time as the data. We believe this form of modeling helps make the numbers understandable as well as the assumptions and certainties that go into those numbers.

Possible Automated Analysis

Once we arrive at the model above, we would have enough information to calculate the value of information (VOI) of additional certainty for each metric. For instance, a reduction of uncertainty of the variable ‘Regular Lemonade at Dolores Park’ to 0 could produce an expected few dollars per hour, assuming that resulting decisions would be made using the model.

The value of new options could also be calculated easily if one could come up with a probability distribution of their expected earnings per hour.

While these kinds of analysis are well established in academia, they are currently difficult to use. If estimations could be simply mapped, it may make them significantly more accessible.

Similar Work

This work can be seen as similar to Unified Modeling Language (UML) in that it attempts to graphically specify a complex system of knowledge. UML was an attempt to define a graphical language for software architecture. There were claims that programs that produced UML could be used to produce their corresponding programs. This hasn’t really happened. The UML spec went through several versions and became so specific and complex that few programmers now bother with it. However, it did encourage the use of whiteboard modeling for other programmers and experiences some popularity with larger projects.

Graphical computer software is challenging. Most attempts have failed, but a few companies have had success with it. LabView is a popular visual programming tool used by scientists and engineers. It uses a Dataflow programming paradigm, which would also be appropriate for Graphical Assumption Modeling.

The theory of this work is similar to that of Probabilistic Graphical Models. These are typically more formal models aimed at computer input and output rather than direct human interaction.

Future Work

This research is very young. The diagrams could use more experimentation and exploration. We have not included a method for subtraction or division, for example. Even if they were better established, it could take a long time for them to become accepted by other communities.

It’s obvious that if these models are useful, it would be valuable to have a computer program to make them. Ozzie Gooen has made a simple attempt called Fermihub. Fermihub is functional, free, and open source. However, it applies only a few simple analytic approximations and does not incorporate Monte Carlo simulations. For accurate or large models, Monte Carlo simulations will be necessary.

There could be more research done in this kind of estimation. While much of the math has already been solved, the art of efficiently creating large models and collaborating with others has a lot of work left. There is also some debate on the proper way to combine estimates, which is crucial for large models.


Note: I realize that the math in the models above, specifically in the combinations of estimates, is incorrect.  I'm currently investigating how to do it correctly.   

References

  1. Fermi Estimates, LukeProg. 2013
  2. See wikipedia for a high level understanding of black boxes. They are a fundamental unit for systems research, which in part has lead to many diagrams we see today.

Understanding Who You Really Are

7 ozziegooen 02 January 2015 08:44AM

Here are 14 ways in which you reveal who you really are. If you’re brave enough, or if you dare, aim to share who you really are, little by little, everyday, with those you trust.

- A typical 'Who You Really Are' article on Lifehack

Take a minute to consider the following questions.

Who are you?
Who are you, really?
Who do you really think you are inside?


It took me a full year to find the answer to these.  The answer was that these questions, when posed as philosophical dilemmas, were bullshit.  This post is not about ‘understanding who you really are’. It's about understanding, 'who you really are'.

“Who are you” is a question that sounds grandiose.  It’s hard to come up with a philosophically solid answer, and this makes it seem interesting.  It is not interesting.  It just lacks context.

What would you say if you were asked “who are you?” by the police?  By a doctor? By a relative? By a potential boss? By a space alien?

You should say different things, because these people would be using the same words to mean different things. 

What they really want is information about you that is of decision relevance to them.   A police cares where you are from. The doctor cares how old you are. A relative cares about who you are related to. A boss cares what skills you have. A space alien cares about your number of eyes and hands.  “Who are you?” really means, “given your understanding of my position, what simple information about yourself do you think is useful to me?”

So when a young philosopher follows up your response with, “no really, who are you?”, you should respond with asking, “what in particular would you like to know?”

Some may respond to this saying that there does exist a true self. A real self.  This is what the phrase should really mean, and this is what I personally spent a year pondering.

But first, the very idea of there being a true self is specific to a set of religions and philosophies that you may not believe in.  If you’re a empirical atheist, you shouldn’t.  David Hume fought the notion of an inner self 250 years ago. [1] Derek Parfit fought it more concretely in the last 30 years. [2]

Second, even if you do ascribe to a belief system where there is some sort of true self, this would not give you a clear way to describe it.  Should you say that you are a Capricorn inside?  Or that a small fraction of your brain believes in Libertarianism?  Or that you possess soul #988334?

Of course not.  The question of “who are you?” is wrongly worded, and the one of “who are you, really?” should be placed on hold until the questioner can figure out what they are actually trying to ask.  

 

[1] David Hume's view on Personal Identity, Skinner (2013)

[2] Reasons and Persons, Parfit (1986)

Why "Changing the World" is a Horrible Phrase

26 ozziegooen 25 December 2014 06:04AM

Steve Jobs famously convinced John Scully from Pepsi to join Apple Computer with the line, “Do you want to sell sugared water for the rest of your life? Or do you want to come with me and change the world?”.  This sounds convincing until one thinks closely about it.

Steve Jobs was a famous salesman.   He was known for his selling ability, not his honesty.  His terminology here was interesting.  ‘Change the world’ is a phrase that both sounds important and is difficult to argue with.  Arguing if Apple was really ‘changing the world’ would have been pointless, because the phrase was so ambiguous that there would be little to discuss.  On paper, of course Apple is changing the world, but then of course any organization or any individual is also ‘changing’ the world.  A real discussion of if Apple ‘changes the world’ would lead to a discussion of what ‘changing the world’ actually means, which would lead to obscure philosophy, steering the conversation away from the actual point.  

‘Changing the world’ is an effective marketing tool that’s useful for building the feeling of consensus. Steve Jobs used it heavily, as had endless numbers of businesses, conferences, nonprofits, and TV shows.  It’s used because it sounds good and is typically not questioned, so I’m here to question it.  I believe that the popularization of this phrase creates confused goals and perverse incentives from people who believe they are doing good things.

 

Problem 1: 'Changing the World' Leads to Television Value over Real Value

It leads nonprofit workers to passionately chase feeble things.  I’m amazed by the variety that I see in people who try to ‘change the world’. Some grow organic food, some research rocks, some play instruments. They do basically everything.  

Few people protest this variety.  There are millions of voices giving the appeal to ‘change the world’ in the way that would validate many radically diverse pursuits.  

TED, the modern symbol of the intellectual elite for many, is itself a grab bag of a ways to ‘change the world’, without any sense of scale between pursuits.  People tell comedic stories, sing songs, discuss tales of personal adventures and so on.  In TED Talks, all presentations are shown side-by-side with the same lighting and display.  Yet in real life some projects produce orders of magnitude more output than others.

At 80,000 Hours, I read many applications for career consulting. I got the sense that there are many people out there trying to live their lives in order to eventually produce a TED talk.  To them, that is what ‘changing the world’ means.  These are often very smart and motivated people with very high opportunity costs.  

I would see an application that would express interest in either starting an orphanage in Uganda, creating a woman's movement in Ohio, or making a conservatory in Costa Rica.  It was clear that they were trying to ‘change the world’ in a very vague and TED-oriented way.

I believe that ‘Changing the World’ is promoted by TED, but internally acts mostly as a Schelling point.  Agreeing on the importance of ‘changing the world’ is a good way of coming to a consensus without having to decide on moral philosophy. ‘Changing the world’ is simply the minimum common denominator for what that community can agree upon.  This is a useful social tool, but an unfortunate side effect was that it inspired many others to follow this shelling point itself.  Please don’t make the purpose of your life the lowest common denominator of a specific group of existing intellectuals. 

It leads businesses to be gain employees and media attention without having to commit to anything.  I’m living in Silicon Valley, and ‘Change the World’ is an incredibly common phrase for new and old startups. Silicon Valley (the TV show) made fun of it, as do much of the media.  They should, but I think much of the time they miss the point; the problem here is not one where the companies are dishonest, but one where their honestly itself just doesn’t mean much.  Declaring that a company is ‘changing the world’ isn’t really declaring anything.  

Hiring conversations that begin and end with the motivation of ‘changing the world’ are like hiring conversations that begin and end with making ‘lots’ of money.  If one couldn’t compare salaries between different companies, they would likely select poorly for salary.  In terms of social benefit, most companies don’t attempt to quantify their costs and benefits on society except in very specific and positive ways for them.  “Google has enabled Haiti disaster recovery” for social proof sounds to me like saying “We paid this other person $12,000 in July 2010” for salary proof. It sounds nice, but facts selected by a salesperson are simply not complete.

 

Problem 2: ‘Changing the World’ Creates Black and White Thinking

The idea that one wants to ‘change the world’ implies that there is such a thing as ‘changing the world’ and such a thing is ‘not changing the world’.  It implies that there are ‘world changers’ and people who are not ‘world changers’. It implies that there is one group of ‘important people’ out there and then a lot of ‘useless’ others.

This directly supports the ‘Great Man’ theory, a 19th century idea that history and future actions are led by a small number of ‘great men’.  There’s not a lot of academic research supporting this theory, but there’s a lot of attention to it, and it’s a lot of fun to pretend is true.  

But it’s not.  There is typically a lot of unglamorous work behind every successful project or organization. Behind every Steve Jobs are thousands of very intelligent and hard-working employees and millions of smart people who have created a larger ecosystem. If one only pays attention to Steve Jobs they will leave out most of the work. They will praise Steve Jobs far too highly and disregard the importance of unglamorous labor.

Typically much of the best work is also the most unglamorous.  Making WordPress websites, sorting facts into analysis, cold calling donors. Many the best ideas for organizations may be very simple and may have been done before. However, for someone looking to get to TED conferences or become superstars, it is very easy to look over other comparatively menial labor. This means that not only will it not get done, but those people who do it feel worse about themselves.

So some people do important work and feel bad because it doesn’t meet the TED standard of ‘change the world’.  Others try ridiculously ambitious things outside their own capabilities, fail, and then give up.  Others don’t even try, because their perceived threshold is too high for them.  The very idea of a threshold and a ‘change or don’t change the world’ approach is simply false, and believing something that’s both false and fundamentally important is really bad.

In all likelihood, you will not make the next billion-dollar nonprofit. You will not make the next billion-dollar business. You will not become the next congressperson in your district. This does not mean that you have not done a good job. It should not demoralize you in any way once you fail hardly to do these things. 

Finally, I would like to ponder on what happens once or if one does decide they have changed the world. What now? Should one change it again?

It’s not obvious.  Many retire or settle down after feeling accomplished.  However, this is exactly when trying is the most important.  People with the best histories have the best potentials.  No matter how much a U.S. President may achieve, they still can achieve significantly more after the end of their terms.  There is no ‘enough’ line for human accomplishment.

Conclusion

In summary the phrase change the world provides a lack of clear direction and encourages black-and-white thinking that distorts behaviors and motivation.  However, I do believe that the phrase can act as a stepping stone towards a more concrete goal.  ‘Change the World’ can act as an idea that requires a philosophical continuation.  It’s a start for a goal, but it should be recognized that it’s far from a good ending.

Next time someone tells you about ‘changing the world’, ask them to follow through with telling you the specifics of what they mean.  Make sure that they understand that they need to go further in order to mean anything.  

And more importantly, do this for yourself.  Choose a specific axiomatic philosophy or set of philosophies and aim towards those.  Your ultimate goal in life is too important to be based on an empty marketing term.

Reference Frames for Expected Value

3 ozziegooen 16 March 2014 07:22PM

Puzzle 1: George mortgages his house to invest in lottery tickets. He wins and becomes a millionaire. Did he make a good choice?

Puzzle 2: The U.S. president questions if he should bluff a nuclear war or concede to the USSR. He bluffs and it just barely works.  Although there were several close calls for nuclear catastrophe, everything works out ok. Was this ethical?

One interpretation of consequentialism is that decisions that produce good outcomes are good decisions, rather than decisions that produce good expected outcomes.12 One would be ethical if their actions end up with positive outcomes, disregarding the intentions of those actions. For instance, a terrorist who accidentally foils an otherwise catastrophic terrorist plan would have done a very ‘morally good’ action.3 This general view seems to be surprisingly common.4

This seems intuitively strange to many, it definitely is to me. Instead, ‘expected value’ seems to be a better way of both making decisions and judging the decisions made by others. However, while ‘expected value’ can be useful for individual decision making, I make the case that it is very difficult to use to judge other people’s decisions in a meaningful way.5 This is because ‘expected value’ is typically defined in reference to a specific set of information and intelligence rather than an objective truth about the world.

Two questions to help guide this:

  1. Should we judge previous actions based on ‘expected’ or ‘actual’ value?
  2. Should we make future decisions to optimize ‘expected’ or ‘actual’ value?

I believe these are in a sense quite simple, but require some consideration to definitions.6

Optimizing Future Decisions: Actual vs. Expected Value

The second question is the easiest of the two, so I’ll begin with that one. The simple answer is that this is a question of defining ‘expected value’. Once we do so the question kind of goes away.

There is nothing fundamentally different between expected value and actual value.  A more fair comparison may be ‘expected value from the perspective of the decision maker’ with ‘expected value from a later, more accurate prospective’.

Expected value converges on actual value with lots of information. Said differently, actual value is expected value with complete information.

In the case of an individual purchasing lottery tickets successfully (Puzzle 1), the ‘actual value’ is still not exact from our point of view. While we may know how much money was won, or what profit was made. We also don’t know what the counterfactual would have been. It is still theoretically possible that in the worlds where George wouldn’t have purchased the lottery tickets, he would have been substantially better off. While the fact that we have imperfect information doesn’t matter too much, I think it demonstrates that presenting a description of the outcome as ‘actual value’ is incomplete. ‘Actual value’ exists only theoretically, even after the fact.7

So this question becomes, then ‘should one make a decision to optimize value using the information and knowledge available to them, or using perfect knowledge and information?’ Obviously, in this case, ‘perfect knowledge’ is inaccessible to them (or the ‘expected value’ and ‘actual value’ would be the same exact thing). I believe it should be quite apparent that in this case, the best one can do (and should do) is make the best decision using their available information.

This question is similar to asking ‘should you drive your car as quickly as your car can drive, or much faster than your car can drive?’ Obviously you may like to drive faster, but that’s by definition not an option. Another question: ‘should you do well in life or should you become an all-powerful dragon king?’

Judging Previous Decisions: Actual vs. Expected Value

Judging previous decisions can get tricky.

Let’s study the lottery example again. A person purchases a lottery ticket and wins. For simplicity, let’s say the decision to purchase the ticket was done only to optimize money.

The question is, what is the expected value of purchasing the lottery ticket? How does this change depending on information and knowledge?

In general purchasing a lottery ticket can be expected to be a net loss in earnings, and thus a bad decision. However, if one was sure they would win, it would be a pretty good idea. Given the knowledge that the player won, the player made a good decision. Winning the lottery clearly is better than not playing once.

More interesting is considering the limitation not in information about the outcome but about knowledge of probability. Say the player thought that they were likely win the lottery, that it was a good purchase. This may seem insane to someone familiar with probability and the lottery system, but not everyone is familiar with these things.

From the point of view of the player, the lottery ticket purchase had net-positive utility. From the point of view of a person with knowledge of the lottery and/or statistics, the purchase had net-negative utility. From the point of view of any of these two groups, after they know that the lottery will be a success, it was a net positive decision.

  No Knowledge of Outcome Knowledge of Outcome
‘Intelligent’ Person with Knowledge of Probability Negative Positive
Lottery Player Positive Positive

Expected Value of purchasing a Lottery Ticket from different Reference Points

To make things a bit more interesting, imagine that there’s a genius out there with a computer simulation of our exact universe. This person can tell which lottery ticket will win in advance because they can run the simulations. To this ‘genius’ it’s obvious that the purchase is a net-positive outcome.

  No Knowledge of Outcome Knowledge of Outcome
Genius Positive Positive
‘Intelligent’ Person with Knowledge of Probability Negative Positive
Lottery Player Positive Positive

Expected Value of purchasing a Lottery Ticket from different Reference Points

So what is the expected value of purchasing the lottery ticket? The answer is that the ‘expected value’ is completely dependent on the ‘reference frame’, or a specific set of information and intelligence. From the reference frame of the ‘intelligent person’ this was low in expected value, so was a bad decision. From that of the genius, it was a good decision. And from the player, a good decision.

Judging

So how do we judge this poor (well, soon rich) lottery player? They made a good decision respective to the results, respective to the genius, and compared to their own knowledge. Should we say ‘oh, this person should have had slightly more knowledge, but not too much knowledge, and thus they made a bad choice’? What does that even mean?

Perhaps we could judge the player for not reading into lottery facts before playing. Wasn’t it irresponsible for falling for such a simple fallacy? Or perhaps the person was ‘lazy’ to not learn probability in the first place.

Well, things like these seem like intuitions to me. We may have the intuitions to us that the lottery is a poor choice. We may find facts to prove these intuitions accurate. But the gambler my not have these intuitions. It seems unfair to consider any intuitions ‘obvious’ to those who do not share them.

One might also say that the gambler probably knew it was a bad idea, but let his or her ‘inner irrationalities’ control the decision process. Perhaps they were trying to take an ‘easy way out’ of some sort. However, these seem quite judgmental as well. If a person experiences strong emotional responses; fear, anger, laziness; those inner struggles would change their expected value calculation. It might be a really bad, heuristically-driven ‘calculation’, but it would be the best they would have at that time.

Free Will Bounded Expected Value

We are getting to the question of free will and determinism. After all, if there is any sort of free will, perhaps we have the ability to make decisions that are sub-optimal by our expected value functions. Perhaps we commonly do so (else it wouldn’t be much in the sense of ‘free’ will.)

This would be interesting because it would imply an ‘expected result’ that the person should have calculated, even if they didn’t actually do so. We need to understand the person’s actions and understanding, not in terms of what we know, or what they knew, but what they should have figured out given their knowledge.

This would require a very well specified Free Will Boundary of some sort. A line around a few thought processes, parts of the brain, and resource constraints, which could produce a thereby optimal expected result calculation. Anything less than this ‘optimal given Free Will Boundary’ expected value calculation would be fair game for judging.

Conclusion: Should we Even Judge People or Decisions Anyway?

So, deciding to make future decisions based on expected value seems reasonable.  The main question in this essay, the harder question, is if we can judge previous decisions based on their respective expected values, and how to possibly come up with the relevant expected values to do so.

I think that we naturally judge people. We have old and modern heroes and villains. Judging people is simply something that humans do. However, I believe that on close inspection this is very challenging if not impossible to do reasonably and precisely.

Perhaps we should attempt to stop placing so much emphasis on individualism and just try to do the best we can while not judging others nor other decisions much. Considerations of judging may be interesting, but the main take away may be the complexity itself, indicated that judgements are very subjective and incredibly messy.

That said, it can still be useful to analyze previous decisions or individuals. That seems like one of the best ways to update our priors of the world. We just need to remember not to treat it personally.

  1. Dorsey, Dale. “Consequentialism, Metaphysical Realism, and the Argument from Cluelessness.” University of Kansas Department of Philosophy http://people.ku.edu/~ddorsey/cluelessness.pdf

  2. Sinhababu, Neiladri. “Moral Luck.” Tedx Presentation http://www.youtube.com/watch?v=RQ7j7TD8PWc

  3. This is assuming the terrorists are trying to produce ‘disutility’ or a value separate from ‘utility’. I feel like from their perspective, maximizing an intrinsic value dissimilar from our notion of utility would be maximizing ‘expected value’. But analyzing the morality of people with alternative value systems is a very different matter.

  4. These people tend not to like consequentialism much.

  5. I don’t want to impose what I deem to be a false individualistic appeal, so consider this to mean that one would have a difficult time judging anyone at any time except for their spontaneous consciousness.

  6. I bring them up because they are what I considered and have talked to others about before understanding what makes them frustrating to answer. Basically, they are nice starting points for getting towards answering the questions that were meant to be asked instead.

  7. This is true for essentially all physical activities. Thought experiments or very simple simulations may be exempt.

Creating a Text Shorthand for Uncertainty

6 ozziegooen 19 October 2013 04:46PM

Most things I find I discuss are highly uncertain, but it can be really confusing and wordy to state that uncertainty in writing. In this last sentence for example I felt the need to write “I find” to point out uncertainty, for example.

First, people are really bad at agreeing on probabilities. So if I say something is “very certain”, that could mean 80% chance to me and 95% chance to you. This is rigorously explained in the Failure of Risk Management (by the same author from How to Measure Anything), where it is explained further to say that this is especially true of risk managers.

Second, there aren’t too many words to use to indicate uncertainty. I find that I need to repeat the same ones over and over again. And when they are used, these words can be quite wordy and confusing.

  • I think that
  • In my opinion
  • It makes sense that
  • There aren’t too many things
  • Perhaps,

Several years ago some people made the language E-Prime in large part to make this uncertainty crystal clear.

E-Prime (short for English-Prime, sometimes denoted É or E′) is a prescriptive version of the English language that excludes all forms of the verb to be. E-Prime does not allow the conjugations of to be—be, am, is, are, was, were, been, being— the archaic forms of to be (e.g. art, wast, wert), or the contractions of to be—’s, ‘m, ‘re (e.g. I’m, he’s, she’s, they’re).
Some scholars advocate using E-Prime as a device to clarify thinking and strengthen writing.[1] For example, the sentence “the film was good” could not be expressed under the rules of E-Prime, and the speaker might instead say “I liked the film” or “the film made me laugh”. The E-Prime versions communicate the speaker’s experience rather than judgment, making it harder for the writer or reader to confuse opinion with fact.

While I do intend to look more into E-prime, it seems like a bit much to use on a routine basis.

A Possible (Written) Solution

I propose that we instead use a symbol at the end of our sentences or propositions to indicate uncertainty.

Choosing the Levels

A scale would have to be created of course in order to indicate what these levels are. My guess is that the optimal (for usefulness, popularity, and accuracy) amount of levels would be around 5-10, especially because we aren’t very good at accessing probability.

Here’s one example that makes sense to me:
0. ~50% 1. ~65% 2. ~80% 3. ~90% 4. ~95% 5. ~99.9%

In cases where something is unlikely, this would just work the opposite way (50% to 0.01%).

Choosing a Symbol

I think that any representation of certainty would have to be achievable with ASCII characters, if not the English keyboard. Here are some possibilities. Each is shown to be representative for a level of 4/5, according to a scale similar to what is shown above.

Non-Numeric forms

  • The universe is expanding.’’’’
  • The universe is expanding.““
  • The universe is expanding.`
  • The universe is expanding. ····

Numeric Forms

  • The universe is expanding. `4
  • The universe is expanding.4*
  • The universe is expanding (~4).
  • The universe is expanding (c~4).
  • The universe is expanding (c4).
  • The universe is expanding ~c4.
  • The universe is expanding (?4).

My personal favorite at this point is to have a number with the tilda sign “~”, with a symbol for indication (like the “c” or “?”). The dashes are be difficult to read and more confusing to newcomers (c3).

Different Kinds of Uncertainty

So far we’ve assumed that the definition of ‘uncertainty’ is relatively clear, but sometimes there are different definitions of uncertainty.

For instance, there’s the certainty of “the existing scientific literature strongly agrees that evolution is true”, and the certainty of “I personally am very certain that the Paleo diet is good, even though others might disagree.”

These could be indicated by different symbols. This would require a small dictionary of symbols/standards, but this may not be very unreasonable.

Say we use ‘c’ to indicate ‘consensus’ and ‘i’ to indicate ‘personal intuition’, and ‘r’ to indicate ‘personal research/rationality’. Not all of these would need to be used in every instance, only the ones that are instantially relevant.

Some statements could be as follows:

  • The universe is expanding ~c5.
  • I’m not likely to do well in finance ~i4c1r2.
  • Polyphasic sleep has a lot of potential ~r4c1.
  • I was a poor math student ~i4r2 in high school, but have learned a lot ~i3r2 since then.

Of course, we’d need a definition for this, which is effectively a standard. For now I’ll call it “Uncertainty Notation V0.1” I’ll try it out in future posts as an experiment. HTML Codes Reference

Meetup : San Francisco: Effective Altruism

3 ozziegooen 23 June 2013 09:48PM

Discussion article for the meetup : San Francisco: Effective Altruism

WHEN: 27 June 2013 07:00:00PM (-0700)

WHERE: 170 Saint Germain Ave, San Francisco

Our first salon, about Effective Altruism groups in Oxford.

The current effective altruist movements seem to be spread between two very specific places. Here in the Bay Area we have Givewell, CFAR, MIRI, Leverage Research, and others. But across the Atlantic, in Oxford, there exist organizations such as 80,000 hours, the Life You Can Save, the Future of Humanity Institute, and several other fascinating groups.

Holly Morgan the Managing Director of The Life You Can Save and is also involved in several other Effective Altruist groups. She's here in San Francisco for a very limited time only, and will be discussing the ins and outs of English groups we should care about.

This will be in a lecture/salon format, with a talk for approximately 40 minutes, followed by some Q/A, followed by friendly discussions.

Come at 7pm, talk will start at 7:20pm.

Sign up here: http://www.meetup.com/Effective-Altruism-Salon/events/125741742/

Discussion article for the meetup : San Francisco: Effective Altruism

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