Author: James Vornov

Reductionism can be powerful.

Through careful study, a component of a system can be identified and its role in the function of the system defined. Manipulation of that component can be shown to affect the system in a predictable way. It’s often possible to generalize- the heart is a pump in mice, cats and elephants. At a molecular level in the brain, the established role of CREB in aplysia neuronal function predicts role of CREB in mouse hippocampus. Human hippocampus? Well we can’t know because the experiments can’t be done, but the body of available evidence generates a strong belief that it does. The scientific fact that KREB is involved in human learning and memory

These scientific theories are not facts, they are statements with a probability of being true and a complementary probability of being false. Scientists recognize this at least implicitly because the method of science is to collect additional data that will either falsify or support the theory. This data will either increase or decrease belief in the truth of the theory. Sometimes theories are completely abandoned. Our earlier belief turns out to have been unwarrented.This is straightforward pragmatism.

It’s a major mistake to ignore the probability element in scientific theory. We don’t even need to consider how reliable the data really is, the nature of these complex systems makes their significance uncertain. The influence of one system component may vary in hard to predict ways because of changes in other components.

And because statements about complex systems are not true or false, we must make decisions based on belief, the probability of the statement being true. Since just about everything we deal with is a complex system, this impossibility of knowing the future is everywhere. Uncertainty can be found in the very structure of the world.

Umbrian Wall, originally uploaded by jjvornov.

These flat, often bisected texture and contrast studies have been a constant in my images since I picked up my Minolta SRT200 in 1979. I’ve always struggled with how easily I can make these. These days I look at an image like this and simply appreciate how the image recreates the pleasure I personally have being in a receptive state looking at the world around me. Its a re-creation, a re-experiencing of a place and time now distant.

In the Frantoio, originally uploaded by jjvornov.

We’re too snow and cold bound in Baltimore for new image capture, so I’m wandering through Umbria again with these D300 images from 2009. The Oz 2.0 techniques are giving me a second chance to see and enhance the light in the captures.

Chaos Theory was developed by exploring dynamical systems in computers. Its worthwhile considering this idea of a system itself in our exploration of deciding better.

Systems theory is an approach to studying a collection of parts by considering them as a whole. Each part has its role to play and some how interacts with other parts of the system. Consideration of systems is vital to understand where uncertainty comes from in our world or in simple clockwork universes like computer programs.

There’s is a much stronger tradition in empirical science of reductionism, understanding the functioning of a system by taking it apart to study its component pieces. The contribution of the part is considered by examining the interaction of that part with other parts of the system. If the functioning of those parts is understood, an overall picture of the system is built up. For example, the biochemical processes of a cell can be understood by analyzing the metabolites and how they are processed by cellular enzymes.

Reductionism has proven an extraordinarily powerful way to understand the world. For the most part, when an enzyme is blocked in a cell, its product disappears and its precursor builds up exactly as expected. You don’t need to know much about the function of the metabolites or the habitat and behavior of the animal. The function of these components is likely to be the same in locust, rat, cat and man.

A powerful reductionist approach is to study simple systems. The study of memory in the brain of humans or even rats remains much too complicated to explain as a system. It was possible to trace the system to particular brain areas like the hippocampus by studying brain injury in man and experimental lesions in rats. But after establishing that a rat without its hippocampal formations can’t remember how to rerun a maze, how can you figure out the circuitry within the hippocampus that stores that memory? And even if you do, how do you trace that function in the full functioning of a rat in a maze with its visual input and motor output?

Eric Kandel took the approach of finding a much simpler system to study memory, choosing the sea slug Aplysia as an experimental model. This classic reductionist approach provided important insights into how connections between neurons are changed by activity and eventually many of the same mechanisms were found to be operating in the rat brain. Eventually manipulation of these mechanisms in rats demonstrated that they were critical for memory formation.

Reductionism often works well in science. It shows that a component or mechanism in one system serves a similar purpose in another system even though these systems may be too complex themselves to understand fully. This can serve as valuable information if it turns out that manipulation of this one particular component has a consistent effect on the functioning of the overall system.

I’ve just finished re-reading Mitchel Wladrup’s Complexity: The Emerging Science at the Edge of Order and Chaos to followup on the Chaos discussion. About halfway through I realized that the book is now 20 years old. Perhaps because it was written so close to the founding of the Santa Fe Institute and was based primarily on interviews with key figures in that exciting flowering of ideas it still provides a vivid read.

I was struck by how little impact these big ideas seem to have had on the usual way of seeing the world. Perhaps chaos, complexity and emergence have entered the language, but dreams of improved prediction tools or appreciation of principles like unintended consequences don’t seem to have been achieved. There was a feeling when the book was written that we were on the verge of new forms of artificial intelligence and new approaches to economics that would help us understand the interconnectedness of the global economy. We have Google and mobile devices like the iPad. Sadly, it seems like more of the same only faster and in more places.

I had hoped that tools of decision theory, modeling and simulation would change difficult research and development projects like drug development. In my current job I get a pretty fair overview of the industry on a daily basis and can report that little has changed.

Insights from behavioral economics and advances in cognitive science that have had less impact one we way we see the world. There’s a constant stream of media reports about the science, but little evidence that these fundamental insights are informing our discussions about human behavior and ethics.

My original impulse when I started writing On Deciding . . . Better in late 1999 was to be at least one voice discussing what I thought were important implications of decision theory and Bayesian approaches to probability theory. Over the years, I’ve explored the sources of uncertainty in the world and most recently the emerging insights of Cognitive Neuroscience. I admit that mostly I write for myself, to get ideas out into better organized form and critically review them for myself.

My view of the value of writing and publishing on the net hasn’t changed in the last decade. I have a free, universally accessible publishing platform for my ideas. I’ve been fortunate over the years to actually have kindred spirits interested enough to read and comment on my efforts. I’ve been further encouraged over the last few months finding how Twitter as a microblogging environment provides a new venue to widen that circle of interaction like a virtual interdisciplinary conference.

The world of ideas is still vibrant. Its bigger and noisier than it was in 1992 or at the founding of the Santa Fe Institute in 1984. Certainly its bigger than the world of physics was at the time of either Einstein or Newton. However, I’m brave enough to suggest that like our world, those worlds also were ruled by a power law dictating the impact of ideas.

Snow Melt, originally uploaded by jjvornov.

This is some of the last snow. Today it looks like a Dr Suess book out there with droopy trees everywhere

There’s a difference between predicting and knowing the future.

Predicting decreases uncertainty but does not eliminate it. We’ll call that level of certainty “knowing the future”. Predicting involves beliefs about the future state of the world and should be probabilistic, dealing in likelihoods of events, often describing a range of possible outomes.

I can make an excellent, accurate prediction about the card that will drawn from a deck. It will be one of the 52 cards in the deck.

Trivial? Not really. I’ve used my knowledge of the nature of card decks to constrain the possible range of outcomes to only those that are possible outcomes.

With more information about the particular deck I’d might be able to narrow the odds of various cards being drawn. For example armed with the exact order of cards in the deck and a historical dataset describing how often each card in each deck position is chosen, I could actually know which card is the single most likely to be drawn. Perhaps if we studied how people tend to draw cards we’d find that the center 25% got 60% of the draws, increasing the probability of those cards being selected over the top and bottom of the deck. Probably I’d be able to provide a list of the cards from most likely to be chosen through least likely.

Just because I know which card is most likely, it doesn’t mean that if the mostly likely card is not drawn in any particular trial that my prediction is wrong or inaccurate. The prediction only allowed me to strengthen my belief in some cards being drawn over other cards.

In selling any prediction service, If I only get one chance to test my ability to predict there’s no way to prove why I happened to be right or wrong. If my knowledge of the order of the deck and human behavior in selecting cards improved my ability to predict the card selected from 1 in 52 to 1 in 20, I’d be more than twice as good at picking the card to be drawn in advance, butits still overwhelming probable that my guess on any particular trial will be wrong.

It makes for a lousy magic trick but a very good way to make better decisions.

The Glamour Tile, originally uploaded by jjvornov.

I’ve probably photographed my front walk more than any other subject in the last few years. There’s a nice combination of textures in the brick and garden elements particularly when there’s been rain or snow.

Today the world here is buried under a few inches of snow with more on the way tonight. If I want to do some more practice with coach Vince (Versace), I’ll need to use some archive images.

In this deterministic world, one of the most important sources of uncertainty is ignorance. In the Schrödinger’s cat thought experiment, we don’t know whether the cat is alive or dead. The source of that uncertainty at the macroscopic level is ignorance. The chamber is sealed and we can’t look inside of it.

From a macroscopic perspective, it’s no different from guessing what’s inside a sealed envelope or any other situation where some state of affairs exists but lack certain knowledge.

From a probability perspective, since we understand the physics of the cat in chamber system well enough, the probability of the cat being alive or dead is known. One could put that probability into a decision tree with a high level of confidence. If the probability of a decay event is 0.5, then the cat is not half dead and half alive, it has a 50 −50 chance of being alive. The event is determined, we just don’t know the answer until we open the box.

I want to completely blur past, present and future when it comes to ignorance since for decision making becaus it makes no difference. If the cat was unlucky enough to die in the first 5 minutes of being in the chamber, then the event occurred in the past we remain uncertain in the present. If we’re making a decision at the 30 minute mark the cat has a higher probability of being alive at present than if we make it at an hour just before opening the chamber, but since we won’t open the chamber until later, it doesn’t matter to us making the decision at 30 minutes. The event may or may not have occurred; ignorance obscures equally from the start to the finish. This is true of the future as well. If we substitute a coin flip to occur in an hour with the same 0.5 probability of success, the fact that the event occurs in the future makes no difference.

Let me also point out that knowledge without communication doesn’t dispel ignorance and thus has no influence on decision making. If we assume that the cat knows that it’s still alive inside the chamber, it makes no difference to us outside of the chamber. The outcome when the box is opened remains uncertain to us. Similarly, if I have a tape or transcript of an event in the past as long as I can’t time travel to influence those events my knowledge has no effect on decisions in the past. I know the text of Lincoln’s Gettysburg Address but that doesn’t affect the choices he had in writing it in 1863.

In the same context, consider the effect of an omniscient God who controls events in the world. Whether its a God that created a clockwork universe that plays out over time or a God that intervenes in events in the time stream, it makes no difference. Whether its a God who knows the full history of creation from the inception of the world to its end or a God that steers events with some ongoing intent also makes no difference. The knowledge and power of this God has no influence on our decisions unless knowledge is granted us about the world in the past present or future. Without knowledge, the veil of ignorance is itself enough to create conditions of uncertainty. As the world is presented to us, our ignorance requires us to make probabilistic decisions based on belief.

Knowledge has positive value in decision making. The more we approach the god-like state of knowing past, present and future, the better we can make decisions. Either probabilistic states can be made to collapse into certainty, like opening up the chamber and determining the state of the cat, or probability estimates can be refined, like checking on the cat halfway through its time in the chamber. Information that is irrelevant to the decision doesn’t help refine the probability estimate. Telling me its snowing outside can’t help refine the probability of whether the cat’s alive when the chamber is opened.

Making decisions under conditions of uncertainty is no different from making decisions under conditions of ignorance. Choosing ignorance over available knowledge is a completely different case