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Outlining Intelligent Design’s Positive Argument

Casey Luskin
Image: Charles Lyell in 1840, by Alexander Craig, Public domain, via Wikimedia Commons.

Editor’s note: We are delighted to present a series by geologist Casey Luskin on “The Positive Case for Intelligent Design.” This is the second entry in the series, a modified excerpt from the new book The Comprehensive Guide to Science and Faith: Exploring the Ultimate Questions About Life and the CosmosFind the full series so far here.

The theory of ID employs scientific methods commonly used by other historical sciences to conclude that certain features of the universe and living things are best explained by an intelligent cause, not an undirected process such as natural selection. To borrow geologist Charles Lyell’s words, intelligent agency is a cause “now in operation” that can be studied in the world around us. Thus, as a historical science, ID employs the principle of uniformitarianism by beginning with present-day observations of how intelligent agents operate, and then converts those observations into positive predictions of what scientists should expect to find if a natural object arose by intelligent design. 

When an Intelligent Agent Acts

For example, mathematician and philosopher William Dembski observes that “[t]he principal characteristic of intelligent agency is directed contingency, or what we call choice.”1 According to Dembski, when an intelligent agent acts, “it chooses from a range of competing possibilities” to create some complex and specified event. Thus, the type of information that we observe results from intelligent design is called specified complexity or complex and specified information — or CSI for short. 

In brief, something is complex if it’s unlikely, and specified if it matches an independently derived pattern. In using CSI to detect design, Dembski says ID is “a theory of information,” where “information becomes a reliable indicator of design as well as a proper object for scientific investigation.”2 ID theorists then positively infer design by studying natural objects to determine whether they bear the type of information that, in our experience, arises from an intelligent cause.

A Large Empirical Dataset

ID thus seeks to find in nature the types of information — to be precise, complex and specified information — that we know from experience is produced by intelligent agents. Human intelligence provides a large empirical dataset for studying what is produced when intelligent agents design things. For example, language, codes, and machines are all structures that contain high CSI, but in our experience, these things always derive from an intelligent mind. By studying the actions of humans, we can understand what to expect to find when an intelligent agent has been at work, allowing us to construct positive, testable predictions about what we should find if intelligent design is present in nature. High CSI thus reliably indicates the prior action of intelligence.

A Two-Step Process

This positive argument for design follows the standard scientific method of observation, hypothesis, experiment, and conclusion. To be more specific, the positive case for design begins with observations of intelligent agents and what they produce when they design things. This leads to hypotheses (predictions) about what we should expect to find if intelligent agency was involved in the origin of a structure. These predictions are testable via studies of nature — often called experiments — but in this case meaning any empirical study of what exists in the natural world. Depending upon the outcome of the experiments and the nature of the data, the hypothesis/prediction is either confirmed or not. This leads to a (tentative) conclusion about whether design has been detected in nature. 

At its simplest level, the positive case for design is a thus two-step process:

  1. Study intelligent agents to understand what kind of information is produced when they act.
  2. Study natural objects to determine whether they contain the type of information known to be produced when intelligent agents act.

Next, “Investigating the Evidence for Intelligent Design.”

Notes

  1. William A. Dembski, The Design Inference: Eliminating Chance Through Small Probabilities (Cambridge, UK: Cambridge University Press 1998), 62.
  2. William A. Dembski, “Intelligent Design as a Theory of Information,” Intelligent Design Creationism and Its Critics: Philosophical, Theological, and Scientific Perspectives, ed. Robert T. Pennock (Cambridge, MA: MIT Press), 553-573.