Dr. Watson

Welcome to Dr. Watson, part detective (think Sherlock Holmes), and part psychologist (think John B. Watson , one of the founders of modern psychology). Dr. Watson is an interactive tool designed to help scientists guide laboratory experiments. At this time, it is oriented primarily toward choosing continuous parameters (concentration, temperature, pressure, time,...) required to run experiments.

We assume experiments are run sequentially (we see the results of one experiment before running the next), and we assume that the experiments take enough time that it is worth thinking about what to do next. We further assume that you are trying to maximize or minimize some metric (strength, lifetime,...)

The tool at this stage is extremely young, with limited functionality. It has been tested primarily using Safari on a Mac, and Chrome on Windows. It does not work at this time with Internet Explorer.

Dr. Watson starts with a Bayesian prior, which means that we work with what you already know. As of this writing, we have to assume that the relationship between your performance metric (say, strength) is a quadratic function of each input parameter (such as temperature or concentration). But we assume you do not know which quadratic function. As the tool evolves, we will be adding new relationships.

Dr. Watson walks you through a series of steps:

Step 1: Belief expression - You use a graphical toolkit to draw a family of possible relationships.

Step 2: Prior formation - Using the family of curves that you have drawn, Dr. Watson creates a mathematical representation of a prior using normal distributions. We create a best estimate, and can show you a set of samples drawn from a distribution we create from the information provided in Step 1.

Step 3: Value of information - We use a tool called the knowledge gradient to identify the next experiment that is likely to produce the highest possible improvement. We show the range of outcomes (in the form of updated beliefs known as the posterior) that would result if you ran one more experiment using parameter settings that you specify.

Step 4: Posterior belief - This is the updated prior after running an experiment. You can run an experiment with a high value of information, or another one that you choose.

Much more to come, but that is all we have developed so far.

Welcome to Dr. Watson, part detective (think Sherlock Holmes), and part psychologist (think John B. Watson , one of the founders of modern psychology). Dr. Watson is an interactive tool designed to help scientists guide laboratory experiments. At this time, it is oriented primarily toward choosing continuous parameters (concentration, temperature, pressure, time,...) required to run experiments.

We assume experiments are run sequentially (we see the results of one experiment before running the next), and we assume that the experiments take enough time that it is worth thinking about what to do next. We further assume that you are trying to maximize or minimize some metric (strength, lifetime,...)

The tool at this stage is extremely young, with limited functionality. It has been tested primarily using Safari on a Mac, and Chrome on Windows. It does not work at this time with Internet Explorer.

Dr. Watson starts with a Bayesian prior, which means that we work with what you already know. As of this writing, we have to assume that the relationship between your performance metric (say, strength) is a quadratic function of each input parameter (such as temperature or concentration). But we assume you do not know which quadratic function. As the tool evolves, we will be adding new relationships.

Dr. Watson walks you through a series of steps:

Step 1: Belief expression - You use a graphical toolkit to draw a family of possible relationships.

Step 2: Prior formation - Using the family of curves that you have drawn, Dr. Watson creates a mathematical representation of a prior using normal distributions. We create a best estimate, and can show you a set of samples drawn from a distribution we create from the information provided in Step 1.

Step 3: Value of information - We use a tool called the knowledge gradient to identify the next experiment that is likely to produce the highest possible improvement. We show the range of outcomes (in the form of updated beliefs known as the posterior) that would result if you ran one more experiment using parameter settings that you specify.

Step 4: Posterior belief - This is the updated prior after running an experiment. You can run an experiment with a high value of information, or another one that you choose.

Much more to come, but that is all we have developed so far.

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