Let’s explore how to gain advantages by comparing analog and digital characteristics of the Information Environment (IE).
First, we consider an overarching conception of the IE that includes the continuous (analog) and the discrete (digital).
As portrayed in various doctrine and concept documents, the IE may be thought of in terms of three dimensions: physical, informational, and cognitive (including human and machine actors).
Physical. The physical dimension of the IE consists of the infrastructure that stores, transmits and receives information. We measure infrastructure in terms of continuous ranges of values — length, width, height, depth, time, energy emissions, etc. These physical aspects are therefore often sensed as analog information, but they can be sensed as increments of discrete, digital information, too. Thinking broadly, the major components include structures and equipment.
Informational. The informational dimension of the IE, which includes cyber and the electromagnetic spectrum, includes the networks and energy fields where data and information is collected, processed, stored, disseminated, and displayed.
We tend to measure information in terms of continuous ranges of values and in terms of discrete values. Consider the following example of measuring the loudness of sound:
We can use analog and digital values to convert qualitative and quantitative data (D) to information (I). Each form has rewards and risks that are subject to variable contexts While analog information can attribute meaning (30 dB is a barely audible whisper), that meaning can change vary (a hyper-sensitive sensor is listening). While digital information is more rapidly transferable (a Voice of America audio stream), its meaning also may change in another context (Vladimir distrusts VOA):
|D to I||Qualitative||Quantitative|
|Analog||dB range categorizes||120 – 140 dB|
|Digital||dB specifies||120 … 140 dB|
Our efforts to measure information in terms of a range of values and specified values require us to interpret meaning in different contexts. In the previous two examples, whether 30 dB is a whisper and whether VOA has its intended effect, depend on technological and human characteristics of the IE. How can we anticipate such variables to our advantage?
One way is to model how the informational and physical dimensions interact. Martin Libicki, in Cyber Deterrence and Cyberwar, explains how the informational dimension connects to the physical dimension (physical layer, as Libicki puts it) in terms of layers of information.
The physical layer is largely taken to be analog, but not at the quantum level of particles and waves. The informational layer is largely assumed to be digital because digitization continues to fuel broad technological advances with profound socio-economic impact.
So, the digital content-and-instruction information resides in an analog physical infrastructure (facilities house clouds), except during wireless transfer. We can anticipate different technological and social contexts changing available information and the meaning of information.
Technological breakthroughs could include device-less storage, which would expand the availability of information for those able to access it. Changed social contexts could include could de-population trends, international migration, and global epidemics. Now we need to consider the cognitive dimension.
Cognitive. The cognitive dimension of the IE includes individual factors and group dynamics among those who create and act upon information flows. There are many ways to measure cognition in terms of functional performance (task comprehension, environmental sensing) and impairment (cognitive domain tests). How cognition works, however, is not systemically understood, though advances in neural information processing has led to artificial neural networks capable of learning tasks.
An important question that bears on anticipating events in the IE is, how will cognition process more information in new contexts? For instance, we anticipate the invention of device-less storage and extrapolate current de-population trends (see Eastern Europe), what conclusions can we draw?
In answering this question, humans are not the only systems that assign meaning to data thereby creating information, and contextualizing that information into knowledge. Machines have become influencers, too, as we rely on their conclusions.
Recently released Department of Defense ethical principles seek to limit the influence of artificial intelligence (AI). For instance, the principle of being Traceable restricts AI methods, data sources, and designs. Implementation is key, as unfettered AI can self-improve in its performance of tasks such as, finding relationships among data. So far, we don’t seem to think that artificial neural networks can intuit.
Each of the above dimensions of the IE has peculiar vulnerabilities to mitigate. Physical components are hard to hide. They tend to be hardened and entry controlled. Informational layers are subject to flaws, disruption and deception. Disguised as benign content, viruses can access a system then release their own instructions. Cognition is susceptible to all manner of misperceptions, data, narratives, motives and expectations. There is also unpredictable agency.
The functional components of these three dimensions interact in known and unknown ways. At this point, we understand the IE as a system within which these three dimensions overlap.
Let’s consider a tangible, information-heavy system—an integrated air defense system (IADS). Major Peter Mattes usefully describes the three main components as air surveillance, battle management and weapons control. To illustrate the value of characterizing the IE in all three dimensions, we will consider just the first component, air surveillance.
The physical functional components of an air surveillance system includes sensors such as radar, infrastructure such as buildings, energy sources such as generators, and electronic components such as transceivers, processors and converters. To understand how the physical components interact, we look at the informational and cognitive dimensions.The informational syntactic functions of air surveillance are the instructions. Mattes has described the instructional functions in terms of detect, initiate, identify, correlate and maintain:
The informational semantic function is the content that the preceding “layer” of instructions is interacting with, such as an object’s course, heading, speed, and altitude.
The cognitive functions of air surveillance include personnel reliability and performance: how well trained, relevantly educated, and motivated are people for the mental challenges of air surveillance? The will and capability to maintain situational awareness must be competitive.
Machine-presented conclusions are part of cognition, too. What if the object being tracked was identified as an airliner in the vicinity of a nuclear power plant, and the object behaved unexpectedly? Is this “unpredictable agency” by the object, or are the machine’s indicators incorrect? What action should be taken and by whom or what?
These are the uncertainties in a dynamic IE, even in a tangible, information-heavy IADS. Analog clocks coexist with digital clouds.
Some of the components in the IE act like mechanical clocks, and others act like conscious clouds. Karl Popper divided the world into these two types of systems, one orderly and renderable into parts, and the other disorderly and irregularly holistic.
We know what goes into clocks, and how their components work together as a closed system to produce results. When the results aren’t what we want, we adjust the parts to fix the problem. The analogy is a bit outdated now with hyper connected clocks and watches, but the point is, mechanical clocks are relatively predictable. Clouds are not.
Clouds are dynamic processes affected by many environmental factors such as wind velocity, temperature, and pressure. Lorenz’s “butterfly effect” showed that sensitive environmental conditions produce “deterministic chaos” — such that a butterfly’s flapping in one location could set in motion interactions that cause a change in the weather elsewhere.
Now let’s relate this chaotic sensitivity to our air surveillance example of a tracked object. Our target is now diving toward the nuclear power plant’s radioactive storage pool. We can anticipate and prepare for such uncertainties, but we cannot predict all failures or random acts. How can we gain advantage in such circumstances?
We have entrenched principles from the clock world, to be sure. The problem is that if we operate the same way in the cloud world as the clock world, we will not produce the same effects. So to gain and maintain advantage in the IE, we need to reconsider conventional wisdom.
The Clock-Cloud comparison below captures how to seize and maintain the initiative. The following concepts that are generally considered ways to gain the advantage of initiative: tempo; momentum; learning; decision; position; and freedom of maneuver. To repeat, in order to gain advantage in a cloud world, we may need to operate differently than in a clock world:
|Momentum||Mass x velocity||Networking|
|Learning||What to acquire||How to acquire|
|Decision||Planning A, B,…||Creating opportunities|
|Position||High ground||Virtual control|
|Freedom of maneuver||Ops access||Info access|
To Gain Initiative (Clock-World example; Cloud-World example):
Freedom of maneuver
Given the preceding characteristics, uncertainties and comparisons in the information environment, how should you operate to gain advantages over threats and competitors?