The model has three independent dimensions, so the model parameters X, Y and Z can be read as points in 3-D space.

To make predictions about the effects of our ad campaign, we will need to find numeric values for X, Y and Z (i.e., 'Fit the model to data'). We might use factors such as the estimated incubation period of the disease under given conditions (call it X), the percentage of people who would wear masks in public given a public health campaign (Y), an estimate of person-to-person transmission likelihood (Z), and so on, to estimate patterns of spread. For example, let’s say we want a model of how a public health campaign would affect COVID-19 spread. The curse of dimensionality is why our estimates of how a disease will behave will always have imprecision.ĭimensions most commonly refers to the space and time we occupy, but it can also mean any set of measurable things that are independent of each other. The reason for this trade-off is the “ curse of dimensionality.” It is not a rule of thumb or a limit due to measurement errors, but as much a mathematical fact as the Pythagorean theorem-and it puts fundamental limits on what economics and other social sciences can describe. It is a reduction of reality with the aim of reconstructing a picture of truth, whether about the spread of disease, the population of a toad species or the number of people who will move in 2020.īut as the number of things we’re trying to study grows, the chance of getting even close to the target reality falls.

A model takes different parameters you are studying and uses them to make some claim about how our world works.

Arguably, all of scientific inquiry in modern times begins with some sort of model.

Heisenberg principle self