Towards a Broader Definition and Understanding of the Human Dimension: Part 3

Towards a Broader Definition and Understanding of the Human Dimension: Part 3

Michael L. Haxton

This is the third in a series of three articles that discusses analytics of the human dimension of conflict. Much is being written on the human dimension of conflict, but much of it is using differing versions of what is important. The first article focuses on defining the concepts for analyzing the human dimension of conflict.  Next, there is a growing reliance on analytics to understand the world around us, but caution must be exercised when applying analytics, especially in complex environments, like the human dimension of conflict.  The second article focuses on proposing a set of important principles for guiding analytics of the human dimension.  Lastly, the human dimension involves are wide range of disparate types, formats, and sources of data.  This involves significant and complex challenges for efforts to capture and organize it.  This last article discusses the types and sources of data that must be accounted for in the human dimension. Part 1 can be found here and Part 2 here.

Data on the Human Dimension

The human dimension includes both physical things, like persons, places, tools, resources, and so on; and it includes an array on non-physical (or imagined if you will) things, such as ideologies, institutions, identity groups, and so on.  The challenge of organizing these very different kinds of things is that the types of physical things change as humans invent new physical things (e.g., cell phones to smart phones, etc.), and the way which non-physical things can be encapsulated is even more challenging for data collection.  Where do we draw the difference between Marxism, Leninism, Maoism, and Communism?  What is the difference between Wahhabists and Salafists, and can we enforce a consistent definition across multiple analysts?  Any solution to this challenge must exhibit flexibility, clarity, and consistency.  This is no easy feat.

The objective is to describe the relevant aspects of the human dimension, specifically:

• Thoughts: what are peoples’ values, beliefs, attitudes, opinions, preferences, motivations, intentions, and so on?
• Behaviors: what are people doing, what are their actions, what are they communicating, etc.? 
• Context: what is the situation people live in, the context surrounding their thoughts and behaviors; what is their wellbeing, the geographic setting, technology, infrastructure, language, culture, religion, ideology, and so on?  

Thought

Data on the human dimension has to deal with peoples’ thoughts.  ‘Thought’ is a generic term that is meant to capture the full range of relevant features of peoples’ motivations and intentions; namely, their values, beliefs, attitudes, opinions, and preferences. They can only be inferred through probing and indirect data, not through direct observation.  For instance, opinion research data probes people to reveal their opinions or preferences on topics of interest (e.g., who will you vote for), but behavioral data can also be used to infer opinions at different levels of analysis, such as data on protests, purchasing patterns, or statements in various venues (public or private).  Sophisticated opinion research teases out true attitudes, etc., using combinations of questions and formats to reveal what the person thinks, not just want they want to tell you.  Likewise, behavioral data cannot simply be taken at face value, sophisticated methods must account for the context of the behavior to understand the underlying attitudes.

The difference between values, beliefs, attitudes, opinions, and preferences is not easily defined.  There is limited agreement across the social sciences with political science, psychology, sociology and other fields defining each uniquely.  For our purposes, we will adopt the construction from social psychology that beliefs are fundamental statements about the world that lead to attitudes and opinions, which in turn result in motivations and intentions that then result in behavior.  In short, we are interested in all of the above when it comes to individuals, groups, and populations of interest.

Data on these facets of thought are crucial to enable understanding of human behaviors.  Getting to understand peoples’ intentions and motivations enables analysts to come to identify courses of action to alter behaviors in the future in ways that only this level of detail can yield.  However, it must be understood that these facets are dynamic and prone to sudden changes to varying degrees.  Values and beliefs are considered to be the most stable of the facets, but opinion can be quite dynamic, and thereby so are motivations and intentions.

Behavior

Behaviors, on the other hand, are the heart of what the human dimension of conflict is most concerned with.  What are people doing, what have they done, and what will they do?  Behaviors are observable to varying degrees and with varying levels of difficulty, depending on the nature of the behavior and the openness.  Simplistically, we can divide behavior into two types: actions and communications.[i]  We separate these two because they involve very different consequences.  Actions are the things that we do and the places we go.  Communications are the things we say and the messages we send.  Actions are of direct relevance to conflict, and while communications are very real, they are only of indirect relevance to conflict.  At different levels of analysis, the aspects of the actions and communications that are relevant differ.  For instance, at the meso level, who we do things with or to matters, as does who we communicate to or the topic of the communication.  Lastly, behaviors are very dynamic and must be updated frequently.

Context

Contextual factors, once defined, are the least challenging to collect.  Context includes those factors that define the situation in which behaviors and thoughts are occurring.  They are the exogenous factors that influence the decisions being made and the behaviors being exhibited.  However, the difficulty in collecting contextual factors increases with the specificity of the context that must be captured.  Capturing the generic descriptions of socio-economic differences, religious differences, and political representation in local government to understand a protest event are much easier than capturing the detailed historical tensions, and the specific families and organizations involved in the protest event.  The more specificity that is required for the analysis, the more challenging the data collection.

• Infrastructure (buildings, roads, utilities, etc.)

• Ecology (water, food, carrying capacity, etc.)

• Institutions

–Political

–Legal/Security

–Social (cultural, religious, ethnic, etc.)

–Economic

–Health

• Culture (religions, languages, ethnic identities, etc.)

• Facets of human condition (wellbeing, health, etc.)

The range of contextual factors encompasses a great deal.  The text box includes a sample of some of the factors that are widely relevant to analytic questions in the human dimension of conflict.  This short list includes very broad terms and even this short list could be made longer.

In general, contextual factors are much more stable and easily captured than behaviors and thoughts. The dynamics of these factors is not where the challenge lies.  The challenge lies in the range of facets that may be relevant across cases.  A simple data collection scheme that defines a closed set of contextual factors of what analysts must focus their attention on is likely to run into difficulties from being incomplete and inadequate.  Across cases, across analytic objectives, and across time, analysts are likely to find a relatively small set of factors recurring, but also a large, open, and changing set of factors that are relevant only sometimes.  The challenge for data collection of contextual factors is to enable an open system of data collection that facilitates reuse of collected data, while enabling the flexibility to adapt coding schemes and data frameworks to the specifics of individual cases.  This involves an inherent tension that must be acknowledged and addressed when designing data frameworks.

Types of Data

There are a wide variety of data sources and collection procedures in the human dimension that must be accounted for. They range from participant observation and ethnographic research methods to data coding of historical accounts of people or events.  Data on behaviors and contextual factors involves observation at varying proximities to the person, people or event of interest.  Data on thoughts involves probing, eliciting and recording responses in people to questions or discussion themes in polls, surveys, focus groups, or interviews.

Data sources and collection procedures in the human dimension differ in a number of ways.  Three relevant differences for our discussion are:

• Remote versus in-person access
• Directness of the sourcing
• Structured to unstructured data

Data collection is either remote or involves direct, in-person access to the subject of the data collection.  Remote data collection may involve using communications technology to gain access or some other means of making the observation.   Remote access is almost always more efficient in terms of resources, but comes at a cost in terms of completeness, confidence, and reliability in the data.

Data collection can also vary in terms of how closely connected it is to the context, behavior or thoughts.  For instance, directly observing an event allows the data collection to have the highest confidence and complete understanding of the event.  However, this is rarely possible and therefore compromise in necessary.  Speaking to witnesses to the event can get the data collection close to the event, while reading journalistic or social media accounts is less costly though less complete and confident. 

Finally, data collection can vary from being highly structured and specific (e.g., opinion polls) to being highly unstructured and flexible (e.g., participant observation).  Surveys and polls allow the data to specifically measure the aspects of the subject the analyst thinks are important, and do so in a consistent and precise manner.  This is most effective when the analyst knows specifically what matters.  Focus groups and field observation methods allow the data collection to explore the subject more openly and without presumption as to what is important.  This is more useful when the analyst is first exploring the subject, and has minimal understanding going in.

Summary

Analysis of the human dimension is of critical importance to US National Security today and for the foreseeable future.  Providing effective analytics of the human dimension is both feasible and cost-effective.  Many agencies in the USG are addressing analytics of the human dimension under a variety of names, including socio-cultural analytics, human terrain analysis, human geography, among others.  All deal with the fundamental requirement to understand the human dimension of conflict.

The data to feed these analytics can come from a wide variety of data collection processes and must be used with the appropriate analytic techniques.  This article has provided a glimpse at the variety of data sources and collection methods that encompass unclassified, classified, and other non-public sources of data and information.  Any capability to conduct analytics of the human dimension should strive to include many of these sources and a variety of analytic techniques, following the principles laid out in the second article in this series.  Combining solid and diverse data sources with sound, mixed method data analytics approaches can yield very powerful analytic capabilities.  In the end, this will yield better understanding of the human dimension of conflict, both left and right of the bang.

End Note