Voter motives and messages
The analytics story behind the Scottish secession vote, “Brexit” referendum and U.S. presidential election.
By Douglas A. Samuelson
Amidst the ongoing debates about the U.S. 2016 elections, the most plausible explanation comes to us from a Greek OR/MS analyst who started off looking at medical research. As often happens, truth is stranger than fiction, and powerful insights tend to emerge from seemingly unrelated lines of thinking.
Several years ago, Dimitris Vayenas’ father was diagnosed with amyotrophic lateral sclerosis (ALS), also known as the disease that killed baseball great Lou Gehrig. Since the disease is highly heritable, this implied that Vayenas himself had about a one-in-six chance of developing it. Naturally, he became curious about it and interested in bringing together the Greek and the Israeli ALS Patient Associations. One thing led to another, and in 2011 he found himself sitting behind two leading French researchers at a conference on ALS at the Weizmann Institute of Science in Israel while a British researcher from the University of Oxford convincingly argued that the French researchers’ line of inquiry was wrong. Why, he asked the French researchers, didn’t they offer to join the efforts? He was promptly informed of the incentives, in grant-funded research, to compete rather than collaborate. This got him thinking about incentives against useful information sharing in many areas of endeavor.
In particular, he shared his concerns with his mentors at the RAND Corporation, professors James A. Dewar and Warren Walker, on transparency and communication in information networks. That led to his matriculation at the Department of Computer Science at the University of Oxford in June 2012, aiming toward the “modeling and objective quantification of transparency” that may help the quantification of each party’s contribution in knowledge-based collaborative efforts. He noted that current formal access control models (ACM) are mainly involved with data instead of users. To their detriment, these models assume that the users will always fulfill their intended roles and obligations, so the access control is unable to deal with threats such as insider misdeeds. He realized that many protection policies and practices focus on single-user, single-purpose access, but the real evolving threats involve many-to-many access. The protective focus, he recommended, needs to shift from one user access at a time to collaborations of users – or, as he put it, from vetting access rights to monitoring ongoing information flows and adapting responses.
He also realized that understanding information requires combining content and context, incorporating deeper and more complex representations of the background and intentions behind the communications. In addressing the question of transparency, the detection of “true” and “false” statements in interactions is of primary importance. However, it is not a necessary and sufficient condition; for example, one party can state only “truths” but can be questioned on why it opted to state these “truths” and not other “truths” in the given timeframe and context. He tested his theories about information protection with an analysis of attempted and potential intrusions into the 2011 United Kingdom census  and invented a verb-based access control method (ACM) in order to offer direct integration of behavioural content with the ACM. This included and incorporated provenance (information sources and paths of origin) in the model. In this way, he transformed by transforming the ACM to focus on activity-experience content, rather than just static access records, to ensure more efficient monitoring of experiences [1,2].
This research led him, in turn, to assessing how adversaries think. The Soviet Union, based on extensive and intensive study, had developed a simple but powerful model of human motivations. They saw people in terms of four basic motivational groups: conflict (power)-based, need-based, joy-based and affection-based. Table 1 summarizes their view.
|Motive based on:||CONFLICT||JOY||NEED||AFFECTION|
|Related Life Activities:||War, Politics, Diplomacy, Shipping, Aviation, Exploration, Sports||Love & Sex, The Arts, Entertainment, Fashion, Travel, Relaxation||Agriculture, Trade & Industry, Engineering, Finance, Cooking, Immigration||Family, Spirituality, Philosophy, Medicine, Philanthropy, Pedagogy, Pacifism|
|Representative Age:||Child||Youth||Middle||Senior Citizens|
|Indicated Propaganda Mechanisms:||Nationalistic Ideals, Political Debate, Militant Rallies,Competitive Sports||Festivals and Carnivals, Literature, Tourism, Educational Exchanges||Trade Unions,Standard of Living, Technical Achievements, Trade shows, Financial Aid||Humanistic and Pacifistic Ideals, Ethics, Anti-defamation, Anti-racism, Charity, Internationalism|
Table 1: Human motives and optimal communication according to propaganda specialists in former USSR.
Source: Georgalas, G., 1967, “Propaganda: The Methods and Techniques of Educating the Masses,” new thesis. (in Greek, translated from an obscure Russian source)
Using this overview of human motivators, the Soviet propagandists had exerted considerable influence over their own public as well as other countries’ policies – and elections. This led to the realization that the Russians, among others, are still engaged in influence-seeking activities along these lines – and if they can do it, so can many others. In his predictive model, Vayenas suggested that the Brexit\Trump campaigns were modeled as addressing primarily the conflict motive, pivoted by the joy motive with only implicit references to the need and altruistic motives. Respectively, the Bremain\Clinton campaigns were modeled as addressing primarily the need motive, pivoted by the affection motive with only implicit references to the conflict and joy motives. (The question of whether the Russian government did, in fact, actively intervene in the U.S. election is the subject of ongoing congressional investigation and is well beyond the scope of this article. It is noteworthy, however, that those who unarguably did influence the election did so in a way the Soviets would have found quite familiar.)
Hence, Vayenas turned his attention to analyzing recent and pending elections and concluded that identifying small groups of voters, understanding their motivations and crafting multiple-mode messages that combined the right motivational elements is the way to if not win, to at least accurately predict, the outcome. The effort started by examining the expression of “truth” in the Scottish Referendum where it sensed that the “no” (to independence) voters were bullied by their communities, and therefore the likelihood of their lying in polls was probable. It is worthwhile to note that “undecideds” were 23 percent in person-to-person polling, 14 percent on telephone polling and less than 10 percent in Web polling; these disparities led him to consider these elections as an ideal proving ground of his approach in determining what the result “ought to be” based on the motives of the public as addressed by the campaigns.
This idea is not new, of course, but the explosive advances in big data, big computing and data collection have made astonishing new things possible. He proceeded to predict, more accurately than most other analysts, the outcomes of the Scottish secession vote, the “Brexit” referendum on whether Britain should leave the European Union and the U.S. presidential election  (see Table 2).
|Election||Predicted||Time of Notice||Actual||Previous Election||Poll of Polls Average (on the date of the prediction)|
|2014 Scottish Referendum||54.7%||Two days||55.3%||N\A||49%/td>|
|2015 UK General Elections:||&nsbp;||Five days||&nsbp;||&nsbp;||&nsbp;|
|Conservatives||36.5% & Outright majority||Five days||36.9%||32.4%||33.6% & Hung Parliament|
|Liberal Democrats||6%||Five days||7.9%||22%||10%|
|2015 Greek Referendum||59.5-63.5%||Ten days||61.3%||N/A||47.5%|
|2015 Greek Elections: Syriza||36%||Three weeks||35.46%||36.3%||31%|
|2016 Brexit Referendum||52%||One month||51.9%||N/A||48%|
|Popular Vote||H. Clinton +2% @ 54% turn-out||July 2016||+2.1% @ 54.6%||N/A||14%|
|Winner||D. Trump due to wins in FL, OH, MI, PA, WI||August 2016||H. Clinton|
Table 2: All predictions as published in D. Vayenas’s Facebook timeline.
Vayenas concluded, “It is hard to avoid the parallels with the myth of Cassandra and the tragedy that the rational forces seem to be unaware of these fundamentals of human motives and their impact in electoral outcomes as it appears that the voters, by and large, remain unaffected by the theatrics of the campaigns in terms of policies presented. Moreover, it appears that, contrary to popular belief, more than 96 percent of the voters make up their mind as soon as the elections are announced; they just either don’t know it or don’t admit it in public by giving the impression to the pollsters that their vote is negotiable up to the moment they reach the ballot box. The polemic against “populism” is a polemic against the essence of democracy as a means of expressing one’s motives as experienced in ancient Athens. The forces of reason, rather than intensifying their polemic against populism, need to take into account all these motives if they are to succeed in avoiding unnecessary surprises with unintended consequences and a way to ensure that any future computer network hacking, from Russia or elsewhere, will be doomed to irrelevance.”
Vayenas is the first to concede that his predictions were shared among his friends and followers in his Facebook profile, were not widely disseminated in advance and are therefore not as fully tested as analysts would prefer. Still, his analysis is sufficiently similar to other studies and less formal after-action reports that it appears to merit serious consideration.
Context from Other Sources
Readers of Analytics magazine and OR/MS Today may recall that building databases via social media, to help assess what messaging would work, was a key aspect of the 2008  and 2012  Obama campaigns, and that Obama also used social media to shape his agenda and messaging after the election . Even in 2008, the main ideas of the micro-targeting method were far from new, as political scientist Eugene Burdick had depicted them in best-selling novels in 1956  and, with more technical detail, in 1964 .
Burdick also discussed some of the troubling moral issues that could arise from an unscrupulous or even malevolent candidate using micro-targeting, especially if the conflict and fear motives dominated. George Reedy, former press secretary to President Lyndon Johnson, pointed out that excessively negative campaigning could undermine the legitimacy of the resulting government . More recently, a RAND study found strong evidence of polarization of the American electorate, both along geographic lines and along interest lines, with serious adverse consequences on the ability of the U.S. House of Representatives to ascend above partisan squabbles to get anything done .
Last but not least, it turns out that both the Brexit vote and the Trump victory can be claimed at least in part by a British consultancy, Cambridge Analytica, which appears to have employed pretty much the methods and analysis Vayenas had independently developed. In its case as in his, there is a basis for some skepticism about how good its predictions really were and how much it was just lucky . Still, the implications if they’re right, and if therefore data-driven polarization-based campaigns will now be the ones that succeed, deserve careful consideration by both researchers and political leaders.
There is substantial evidence, from Dimitris Vayenas and others, that models of human behavior based on relatively simple deep motivations provide a basis for powerful emotional appeals that can influence elections and other actions. These motivators seem capable of swamping appeals to reason alone, rendering much policy-based advocacy irrelevant and ineffective. In addition, such motivators explain threats of intrusion to general information and communication systems better than traditional forms of assessment, indicating a different approach to information security. Both the political and information security applications call for more of a focus on many-to-many network communications and less on one-to-many mass messaging. In short, the changing communication methods of our time are having profound effects on our governmental structures, effects we are only beginning to recognize.
Doug Samuelson (email@example.com) is president and chief scientist of InfoLogix, Inc. in Annandale, Va. He is a longtime member of INFORMS.
- Vayenas, Dimitris, 2015, “A Policy Analysis Approach to the Convergence of Formal Methods for Content and Context Modelling: A Verb-based Access Control Model,” Technical Report submitted to Department of Computer Science, University of Oxford and presented at MANCEPT, 2015.
- Gonzalez-Manzano, Lorena, Slaymaker, Mark, de Fuentes, J. M., and Vayenas, Dimitris, “SoNeUCONABCPro: an access control model for social networks with translucent user provenance,” submitted at Lecture Notes in Computer Science: ACNS 2016.
- Vayenas, Dimitris, 2016, unpublished communication to the Financial Times of London.
- Samuelson, Douglas A., 2008, “Election 2008: How to Predict the Winner and How He’ll Do,” OR/MS Today, October.
- Samuelson, Douglas A., 2013, “Analytics: Key to Obama’s Victory,” OR/MS Today, February.
- Samuelson, Douglas A., 2009, “Change We Can Blog In: Obama’s Use of Social Media to help Him Govern,” OR/MS Today, February.
- Burdick, Eugene, 1956, “The Ninth Wave,” Houghton Mifflin.
- Burdick, Eugene, 1964, “The 480,” McGraw-Hill.
- Reedy, George, 1970, “The Twilight of the Presidency,” New American Library, Cleveland, Ohio.
- Sussell, Jesse, and Thomson, James, 2015, “Are Changing Constituencies Driving Rising Polarization in the U.S. House of Representatives?” RAND Corporation Report RR896.
- Wood, Paul, 2016, “The British Data-Crunchers Who Say They Helped Donald Trump to Win: Are Cambridge Analytica Brilliant Scientists or Snake-oil Salesmen?” The Spectator, UK, December. http://www.spectator.co.uk/2016/12/the-british-data-crunchers-who-say-they-helped-donald-trump-to-win/.
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