Voting, Psychology and Communication

“What are we passing down to the next generation? Are we passing down our cultural wealth? Or are we passing down our liabilities?”

JANE ROLAND MARTIN

In its current state, Democracy does not encourage self-review of governmental communication and voting systems, although industry has proven that self-review produces high levels of efficiency in the corporate sector. Rather, citizens are asked to integrate their changing thought—and possibly aberrant opinion—into a static, governmental structures. Perhaps the safety of comfortable paradigms in a rapidly changing world outweigh the value of updated policy change. Our existing systems of governance leave little room for emotive relationships, dialogue or dissent, despite the fact that campaigning politicians often evoke emotion and a spirit of dissent in their speeches. In 2005, Princeton psychologist Alexander Todorov noted that voters’ selections had more to do with subjective perceptions than with objective, political facts (Todorov, 2005). Given this democratic environment, methods and content of communication demand close study.

There are many subtle influences on the way we think and how it affects our decision making. Some of the influences are primitive, like how we react when we identify and meet a potential mate. Others are environmental, like the influence of food or allergies. Some others are in our DNA design. Our combination of influential variables make us who we are. Being aware of these variables will help us make better group decisions.

Voters most often base their decisions on qualitative impressions, such as competence, rhetoric and affability. Todorov suggests that such impressions could derive from considerations of facial features alongside more reliable (or more scientific) forms of data and logic. Todorov’s study indicated that election results might, in large part, come from what Nobel-winning psychologist Daniel Kahneman calls “fast, unthinking judgment” or what psychologist Nalini Ambady calls “thin-slice judgment: the ability to make any number of social judgments from a seconds-long experience” (Ambady, 2010). Like Ambady, Todorov found that citizens make political decisions—such as who they will vote for—very quickly and that increased time for consideration made little impact on their choice. He also found that participants in his study could better judge a candidate’s competence and qualifications in the absence of images of the candidate. Without access to quality information about candidates, and without meaningful communications with politicians, individual voters rely on subjective perceptions, in place of data. Emotional reactions to campaigns act as poor replacement for real knowledge about policy platforms and political history. Contemporary citizens face diverse demands on their time and conducting individual research into pertinent political debates is simply not feasible for many voters. Citizens govern the government by selecting a number of its members. However, this responsibility is not supported by allotted or paid time and resources to research candidates. In populated jurisdictions, especially, the likelihood of meeting candidates in person is very low. Assessing candidates for office in a given election cycle, then, is largely a matter of guesswork, in the current state of Democracy. Liquid forms of voting strengthen exchanges of information about political issues by optimizing personal networks, through which private considerations become public conversations.

Psychological researchers use the term “algorithm aversion” to describe the phenomenon whereby humans prefer traditional or instinctual knowledge to the solutions and predictions provided by computer intelligence. For example, while Google Maps can often provide the most direct travel route, a user may feel that by relying exclusively on Google’s service, they will miss out on local knowledge (shortcuts or scenic routes) that lies beyond the reach of computer programs. Despite risks that can arise from human error—wrong turns, misdiagnosis and profit-loss, for example—many people insist upon maintaining human agency in governmental and corporate decision making. Researchers Dietvorst, Simmons and Massey have found that humans forgive occasional errors on the part of human decision makers but come to distrust computer algorithms more easily, based on even lower rates of error (Dietvorst et al, 2015). In computer programs for chess, route-mapping, medical diagnosis and other areas of analytics, decisions made by complex computer algorithms prove more reliable than those made by humans. In light of this, why do we continually distrust computer intelligence? Why do we have “algorithm aversion”? The primary cause is that most humans do not understand the way in which computers analyze information to arrive at predictions and solutions.

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Evaluating Qualitative vs. Quantitative Data

“The intuitive mind is a sacred gift and the rational mind a faithful servant. We have created a society that honours the servant and has forgotten the gift.”

ALBERT EINSTEIN

As in the political realm, communication systems and data analytics support the effectiveness of business enterprise. Corporate leadership makes decisions based on collected information, both quantitative and qualitative. Quantitative data comes in the form of hard numbers: sales sheets, accounting of overhead and profits that detail historical performance and future earnings. Business responds to this kind of information with analysis, measurement and projection. The other stream of information that informs business decisions comes in the form of qualitative data (also known as intangible value), which executives respond to with instinct honed over years of practical experience. This second source of information cannot be measured with exactitude but businesses who neglect intangibles do so at their own peril.

Business leader Warren Buffet emphasizes the criticality and intangibility of qualitative values as follows:

Businesses logically are worth far more than net tangible assets when they can be expected to produce earnings on such assets considerably in excess of market rates of return. The capitalized value of this excess return is economic goodwill. (Buffett, 1983)

On a similar note, Richard Branson addresses the importance of intangible value in the music business, when he says: The music industry is a strange combination of having real and intangible assets: pop bands are brand names in themselves, and at a given stage in their careers their name alone can practically guarantee hit records. (Branson, n.d.)

Buffet and Branson both recognize that the total value of a successful business surpasses its tangible assets. Businesses that value both quantitative and qualitative factors increase their value. By the same token, neglecting intangible factors may devalue a company. Assets that resist quantification—such as Research and Development costs or brand recognition—do not figure prominently in businesses analysis that focuses on the hard numbers of overhead vs. sales. A 2008 study, conducted by Hulten and Hao, finds that formerly excluded, intangible assets represent 40 to 50 percent of the market value of R&D intensive companies in the U.S. Such assets appear significant in explaining the market-to book-value puzzle (Hulten & Hao, 2008). Questioning which values can be measured, or which metric will most accurately determine those values, challenges established notions of political value and social capital. Making a decision, casting ballots or assigning roles are processes of evaluation and measurement on two levels: fact and feel. In a voter’s’ analysis of options, personal beliefs combine with perceptions of a candidate’s fitness (his or her historical performance, competency, lineage, party and background).For some voters, even a candidate’s visual appearance, dress and bone structure may affect a voting decision.

Too often, the difficulty of applying a metric to intangible, qualitative values (such as “trustworthiness,” “drive” or “charisma”) reduces their effect in decision making and shifts the emphasize to variables that can be measured with a greater degree of confidence. At the end of the 19 th century, Francis Galton stated “until the phenomena of any branch of knowledge have been submitted to measurement and number, it cannot assume the status and dignity of science” (Galton, 1879). American psychologist James Mckeen Cattell stated that, “Psychology cannot attain the certainty and exactness of the physical sciences unless it rests on a foundation of experiment and measurement” (Cattell, 1890). Galton’s firm pronouncement was based upon the work of his predecessors, especially the Medieval scholar John Duns Scotus and the German philosopher Immanuel Kant.

Science seeks to discover governing principles in nature: structural operations, attributes and their interrelation (causal or otherwise). Where only qualitative information exists, science is limited in its predictive power. If intangible values could be measured, great advancements in knowledge would follow because accurate measurements reveal previously unseen patterns of behavior. That which is not measured often goes unnoticed. An eye for talents such as imagination and instinct is often gained through experience, although that talent may go unobserved because it does not show up in statistics or annual reports.

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Social Capital & Trust

Social capital, as a concept, is a relatively new term defined as the value derived from the total of one’s social networks and community activity. Social capital, then, includes personal and professional relationships (in physical or virtual form), social networks and support, civic engagement and belonging or membership to specific groups (from fraternities and societies to boards and neighbourhood watch groups). Social capital also includes the benefits generated through these connections and actions. In the early days of social capital research, the concept was applied exclusively to the social potential of an individual (in characteristics such as charm, sociability, affability and usefulness to neighbours). More recently, the influential sociologist Robert Putman has reframed social capital into an attribute of collectives. He focuses on social norms and trust relationships as producers of social capital. For Putnam, social capital benefits the individuals who possess it as well as the wider community of which they form a part. Social capital is germane to our present considerations, because of its positive contribution to a range of societal factors that sociologists measure, such as personal well-being and crime rates. Increased social capital leads to benefits on many levels: individual, community, regional, national and global. Social capital has been recognized as a driver of economic growth because an increase in social capital results in greater economic efficiency.

“Social Capital has been recognized as a driver of economic growth, resulting in greater economic efficiency” (Putnam, 2000, and 1993; Fukuyama, 1995). At a macro-level, it is likely that higher levels of trust and cooperative norms reduce transaction costs, thereby driving productivity (Putnam, 2000, and 1993). At an individual level, people with wider social networks are more likely to find employment (Aguilera, 2002), to progress in their career (Lin, 2001) and to earn high wages (Goldthorpe et al. 1987). The importance of social capital was recently acknowledged by the Bank of England governor Mark Carney, who stated that “prosperity requires not just investment in economic capital, but investment in social capital” (Carney,2014). World Bank efforts to estimate the “true wealth of nations” suggest that intangible capital, made up mainly of human and social capital, represents around 60-80 per cent of true wealth in most developing countries (World Bank, 2006). However, social capital stocks are not presented as monetary values in this article. Although some researchers have tried to estimate the value of social capital assets as a proportion of total wealth (Hamilton and Liu, 2013), social capital differs from natural and human capital as it is a broad concept, based largely on interpersonal relationships.

The question of measurement in the social sciences forms an important consideration for any inquiry which attempts to quantify social capital. In fact, the subject of measurement in psychology and the social sciences has been surrounded by a great deal of controversy. Today, measurement in the social science is notably different from ideas of measurement in other science disciplines (Michell, 1997). Psychologist Joel Michell writes that “even though quantitative psychologists (by whom I mean those who either theorize about or attempt to measure psychological quantities) hypothesize that their attributes are quantitative and, so, commit themselves to the concept of scientific measurement, the definition of measurement actually endorsed by most of them is radically different”. Curiously, while science has applied techniques of measurement to every conceivable phenomenon in the galaxy, very few scientists have pursued measurement itself as an area of investigation.

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Social capital is an aggregate concept that addresses not only interactions with a group but also individual behavior, attitude and predisposition. As alluded to above, the problem of measuring or even estimating trust in a social network is a very interesting and challenging one. Trust, as an aspect of social capital, remains both undefined and poorly understood. If psychological attributes such as trust cannot be quantified, then the field of social science cannot benefit from the power of mathematical analysis that has proven so valuable in so many other fields of science. The Merriam Webster dictionary defines trust as the belief that someone or something is reliable, good, honest, effective, etc. The online psychology dictionary defines trust as confidence a person or group of people has in relying on another person or group In a social context, trust typically refers to a situation characterized by the following relationship. One party (the “trustor”) consents to rely, in good faith, on the future actions of another party (the trustee). The trustor, then, transfers personal control to the trustee. Since trust is based in assumptions about the personal character and competence, trust always contains an associated degree of risk. Always present in ideas of trust, one finds the opposite: the possibility that the trustee could fail and bring about disappointment or harm (distrust). The trustor’s expectations can only be validated or dashed by experiencing or witnessing the results of the trustee’s completed action.

Harnessing & Harvesting Value

“Obviously I don’t vote as I believe democracy is a pointless spectacle where we choose between two indistinguishable political parties, neither of whom represent the people, but the interests of the powerful business elites that run the world.”

RUSSELL BRAND

The complex phenomena of social capital will be of much less use to us if we are unable to measure it. At some point in the future, social capital may turn out to refer to a key aspect of social physics that underpin both wider societal dynamics and person-to-person interactions and relationships.

Measurement of social capital also ensures that the phenomena are attended to in policy and public good decision making processes. Consistent measure also allows for comparison over time and from place to place. It is possible to develop, a suite of social capital instruments calibrated so that research can be meaningfully generalized.

A designed social metric would also have to synchronize with the fundamental problem of citizen-to-government communication and requires a structural analysis of decision making in the age of the internet. If Democracy is premised on governments making decisions on behalf of the populous, how digital technology could facilitate decision making, the inclusion of a social metric and communication becomes a critical question for political scientists and community leaders. This section of the essay considers the structure of how society interacts with digital technology and how to best integrate a scale into this interaction.

Stanley Smith Stevens, the preeminent Harvard psychologist, was a leading figure in developing a theory of measurement, unique to the social sciences. He first introduced his theory of measurement in a 1946 article, which appeared in the journal Science, entitled “On the Theory of Scales of Measurement.”

His theory was further codified in the influential 1951 Handbook of Experimental Psychology. Stevens defined measurement as “the assignment of numerals to objects or events according to some rule” This definition contested the established definition of measurement, guiding other scientific pursuits, as the ascertainment of the weight, size, temperature (attributes, in brief) of some object or event by comparison to a standard unit. For example, one modern definition, aligned to the physical sciences, conceives measurement as the numerical estimation and expression of the magnitude of one quantity relative to another (Michell, 1997). To understand Stevens’ peculiar definition of measurement in the human sciences requires an understanding of the social context in which his definition arose (as well as of the culture of science during the early twentieth century). Stevens’s definition of measurement was a response to the British Ferguson Committee whose chair, A. Ferguson, was a physicist. The British Association for the Advancement of Science appointed the committee in 1932, to investigate the possibility of quantifying sensory events. Stevens belonged to a school of thought called Logical Positivism which attempted, in the tradition of Decartes, to exorcise all unverifiable ideas from science. Stevens practiced what was known as operationalism. In fact, Stevens contributed a definition of operationalism to Dagobert D. Runes’ 1942 Dictionary of Philosophy, defining it as “the doctrine that the meaning of a concept is given by a set of operations” On this basis, objects and events under investigation will appear to conform to our operational definitions of them. An example of the limitations of this view is E. G. Boring’s famous definition of “intelligence” as “what intelligence tests test” In his lab work, Stevens discovered that the most direct way of measuring the perceived intensity of sensory stimulus—such as light, sound, smell, or electric shock—was simply to ask people to assign a number value to their perceived experience. Participants of his studies assigned numbers to experience without perceived limits on the scale (such as “between one and ten”). Neither did Stevens offer choices between limited number sets or specify the units for numerical values. Sensations measured in this uncontrolled fashion displayed an orderly relationship to the physical magnitude of sensations, a phenomena that is now named Steven’s Power Law, in his honor. Steven’s Power Law is an exponential relationship which equates subjective sensation with the quantifiable, physical magnitude (stimuli) raised to a constant power. The exponent in the equation acts as a unique constant, representing the particular kind of stimulation such as a particular taste to the palette, brightness of a light or loudness of a sound. Stevens characterized and measured countless stimuli using this power law. Today, the human sciences still teach Steven’s four levels of measurement to introductory psychology classes. Stevens’ Power Law is not limited to physical or ratio measurement but includes rankings and interval scales as well. While Stevens worked to legitimize physical experience by developing methods of quantifying it, today the question remains whether subjective attitudes and beliefs could be directly quantified (i.e., expressed as a ratio of some fixed unit).

Innovating communication and decision making systems begins with a review of how organizations gathering substantive qualitative and rigorous quantitative data. Because social impact and sociability are so difficult to measure, such qualitative factors are often overlooked by leadership. This issue highlights the tensions that exist between the dominant cultural narratives of economic progress and the sometimes less visible and less easily quantified importance of social well-being. While technology can assist with some aspects of environmental sustainability, human behavior (at individual and collective levels) will profoundly shape our future, just as it has shaped our past (Owen, 2010).

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