DOI: 10.31038/ASMHS.2019324

Abstract

Listening and reading to the partisan politics surrounding the ongoing conflict between the Palestinians and the Israelis, one gets the impression that this topic is all-consuming. A study using the science of Mind Genomics reveals that most respondents from a sample of young respondents do not care about the topic, nor are engaged by anything said in the media. The Mind Genomics study combined messages, policy statements, issued by the US government, presenting small vignettes, almost as news stories. The strategy prevents the respondent from responding in a politically-correct manner. The data suggest that most of the young respondents are not interested in the topic, when the data from the total panel is reported. Two mind-sets emerged, one feeling that stability and hope will be achieved through force, the other feeling that stability and hope will be achieved through economic development. The study presents a PVI (personal viewpoint identifier) to assign new people to one of the two mind-sets. The paper finishes with a discussion of the contribution of Mind Genomics to a deeper understanding of political thought and the emerging discipline of counter-factual history.

Introduction

A Long History, a Wide Scope, Inflamed Emotions

Depending upon one’s ethnic group and perhaps political / social leanings, the ongoing tensions in the Middle East, and especially between the Palestinians and the Israelis occupy either a great deal of one’s attention and concern, a moderate degree, or little at all, being even perhaps irrelevant. When talking to young people who are not politically involve and polarized, it is hard to discern the nature of the aspects which may concern them when faced with general indifference. In this exploratory paper it is impossible to deeply understand a topic which has been simmering for 70+ years, but it is possible through the science of Mind Genomics, described below, to take a ‘snapshot’ of the situation from the minds of younger people as they respond to statements about possible policy towards the Middle East.

This study represents an exploration of responses to policy statements that have been made, as well as ideas about potential economic help for the Palestinians, ideas that have not been made public, but have floated around. The focus of the study is on the degree to which any of the official statements has the power to influence young Americans, ages 17–30, either as statements which could lead to peace or which could fan the flames of war.

The published literature on the topic of Palestine vs Israel and vs the United States, as well as the actual details of the workings of the Palestinian state come in the form either of books and periodicals, or from news releases and the accumulation of a body of news both from the main mainstream media and now from the Internet. Books dealing with the topic are typically either learned articles or popular books dealing with the topic, or clearly partisan treatments of the topic, the appellation ‘partisan’ being a description of the emotions, not a judgment of the correctness of the treatment [1–4]. A list of the books dealing with the subject would stretch from the 1950’s to today, with no end in sight, as the Middle East continues to occupy the attention of the world, the eruptions in the Middle East, whether minor or major, causing discomfort, and then fear for the disruption of the world order.

When it comes to deeper understanding of the subjective feelings, there are the innumerable public opinion polls about every aspect of the situation, these polls taken often in Israel, and often in the rest of the world as well. The answers emerging from those polls comprise ‘factoids’, with a subsequent attempt tie together the factoids discovered in the polls, linking them to other information about the topic. Yet the deeper understand could become even deeper with better tools, providing a sense of the inside of the mind, in a way metaphorically analogous to the way the MRI technology provides a sense of the nature of body tissue. That will be the topic of this paper.

Polling versus Experimentation

The typical world of responses to public policy invokes the now-institutionalized business and discipline of opinion polling. Scarcely a day goes by that another people comes out to present to the thinking public what the people ‘think, believe, and do.’  The professional associations such as AAPOR (American Association of Public Opinion Research) for example, serves as a bedrock to validate the efforts of the pollsters who present their approaches as scientific. Public opinion research is presumed to be scientific, working with representative samples of respondents, statistical tests of different from the discipline of inferential statistics and of course best practices about how to phrase questions, how to phrase answers, and so forth.

A recurring key issue is that when one does a poll, one invites the respondent to invoke a ‘mental editor,’ to be politically correct.  This mental editor thus skews the results, a skew that cannot necessarily be overcome by the best of the recommended practices. The researcher MUST ask the respondent questions in a direct fashion. Once the respondent can address each question, one question at a time, it is possible and indeed quite straightforward to adopt a stance, tailoring one’s responses to accord with that stance, whether the stance, the point of view truly represents or does not represent one’s inner feeling.

In contrast to polling and surveys or asking the respondent to describe or vote, there is the world of experimentation. Admittedly, experimentation is far easier when one can control the stimulus, varying the stimulus in predefined ways, measuring the responses, and attributing the ‘change’ in the dependent variable to the precise change in the controlled, independent variable.    This approach works very well to understand the dynamics of simple things, such as how the physical ingredients of a food or beverage drive our liking, or the relation between the number of hours one studies for a test and the score on the test

Can experimentation be applied to understand the Palestine-Israel issue? Certain one cannot easily vary the features of a nation in the same way that one can vary the amount of sweetener in a beverage. Yet, what if one were to describe a social situation in different, systematically varied ways. What would happen if one presented a positive, upbeat message versus a negative, downbeat message? How would people react?

The foregoing idea notion involves experimenting with ideas, metaphorically combining the notion of experimentation using statistical design in the spirit of creating and testing aspects of notion of counterfactual history. Instead of presenting history or world issues as simple ides with aspects to be evaluated and discussed, we create scenarios or combinations of ‘facts’ about the issue, these facts having actually happened, or could have happened. We instruct respondents to read these ‘counterfactual combinations,’ vignettes, and respond to them. The respondents have no idea whether what they are reading is true, not true, possible, impossible, or even whether what they are reading comes from a completely different topic or subject area, merged into the set of stimuli.

The respondents simply evaluate these vignettes, from which we learn a great deal about how they perceive the situation and its features.  We do not ask the respondent to act in a rational way, but rather present the respondent with the vignettes, and acquire their responses, at an almost intuitive level, a way labelled by Nobel Laureate Kahneman as ‘System 1’ [5]. The actual inspiration for such an approach comes not so much from a deep philosophical inspiration as it does from the way a company learns how to say the right things to the customer, for example, when marketing Jello^®.

The systematic experimentation opens a whole new branch of social science, akin to the alternative worlds create by science fictions. We are simply creating alternative situations, alternative realities to be tested. The approach, first used for marketing [6,7], and now for social issues, may be the experimental version of ‘counterfactual history.’

How Mind Genomics Studies Problems

It is clear from everyday life that people hold different views about the same topic. The differences can be vanishingly small, or dramatic. One need only read accounts of the same event in different media outlets to recognize the power of the report to present the same idea as an opportunity, and another report to present the same idea as the impetus for a debacle.  Understanding the differences in the points of view of the reporters is clear, and can be done by the analysis of language, and so-called ‘sentiment analysis’ [8].

What is harder to understand is the deep response of ordinary people to the messages which describe a situation. What is meant here is not the initial, almost knee-jerk reaction to the message and the situation, but rather the deep, often automatic response to the message.  Do people really pay attention to the individual messages in politically motivated writing, or is their response simply a generalized, undifferentiated reaction?

Mind Genomics enables us to understand the deeper responses to test stimuli. Mind Genomics does so by mixing and matching different messages, different ideas, into simple to read combinations, vignettes, and then secures the response of individuals to these vignettes, these mixtures. The vignettes being as they are, combinations, defy the desire of the response to be ‘correct’ because the compounding of the messages leaves the respondent unable to formulate what is a consistent response. The desired consequence is that respondent ends up abandoning the desire to be rational, correct, and consistent, and simply answers at almost an intuitive, ‘gut level.’ It is at this intuitive level that one’s true feelings emerge, guiding as they do the selection of the response (Moskowitz, 2012).

The Process of Mind Genomics

Mind Genomics follows a set of well-choreographed steps, beginning with the selection of cognitively meaningful stimuli (messages which are presumed to have real meaning), and ending with the discovery of possibly new-to-the-world mind-sets, different ways to think about the same problem. The phrase ‘Mind Genomics’ is a metaphor for the discover of these different mind-sets for a situation, similar to the discover of alleles for a gene.

Step 1 – Define the topic: It may seem very simple to ‘define a topic,’ but it is not. Mind Genomics operates at the granular level. A topic, for example, cannot be an overwhelmingly large issue like the entire Palestine-Israel issue. If anything, the topic should error on the side of being very small, localized, specific, yet with different aspects.  Mind Genomics could do a good job on a topic such as how to arrange a seating room for a conference between Palestinians and Israelis.  For this topic, we focus on the reactions of young people to pronouncements about the Palestine-Israel situation, made by the State Department, a topic which is large, but not overwhelmingly so.

Step 2 – Define four questions to be answered, with the structure that at some level the set of questions are arranged to ‘tell a story’:  In every topic there exist many types of stories to be told. The objective of Mind Genomics is to determine what specific elements of the story, what messages, resonate with the respondent. In order to discover these resonating elements, we must embed the messages into a story, doing so by means of a method which combines the messages in a structure.  The overriding structure, the story, must make intuitive sense, even when the individual messages when thrown together do not necessarily make sense. Hence, the four questions, which may be likened to the way a reporter structures a story (who, what, where, when, why, how).  The four questions are left to the researcher. They will never be shown to the respondent, but rather they will serve as prompts to help develop the specific elements.  Table 1 shows these four questions for our study on the language of pronouncements about Palestine and Israel.

Table 1. The four questions and the four answers to each question.

Step 3: Create four answers to each question: The answers, short statements or phrases, are the material that Mind Genomics combines, to produce the vignettes. The answers may be actual statements, possible statements, and even new ideas to be explored within the study. Table 1 presents the four questions, and for each question the four answers. Questions 1–3 and their answers were taken from news stories. Question 4 and its four answers were exploratory ideas developed by author Moskowitz over the past decade, but do not yet exist, at least in a well-recognized public forum.  It is important to stress that one need not use the precise words, especially when the language is the less than ‘precise and punchy’ language of diplomacy and reporting favored by government.

Step 4: Combine the elements into an experimental design: The experimental design can be likened to a book of interconnected recipes, with each combination defined as having a specific answer from each of the four questions. An example of the experimental design appears in Table, which specifies the first nine combinations for Panelist #1. The experimental design ensures that each element or answer appears equally often, and that each element is statistically independent of every other element, allowing for OLS (ordinary least-squares) regression to be applied to the dataset. The vignettes are ‘partial,’ meaning that some vignettes or combinations lack an answer from one or two of the four questions. This lack is deliberate, ensuring that the coefficients estimated by the regression modeling have ‘absolute value,’ i.e., the ratios of coefficients are meaningful [9, 10,11].

Figure 1 presents a screen shot from one of the vignettes. The vignette is laid out to be simple, viewable on either a smartphone or a tablet/PC with the layout appropriate for the screen on which it is viewed, and to be available on any platform.  The format is set up to be stark, with one element atop the other, lacking connectives. This format may seem a bit different from the more conventional ‘dense’ format of concepts with the proper language, paragraph form, and replete with connectives. The rationale for the stark format is that the format leads to easier ‘grazing’ for information, and is less tiring, especially when the respondent evaluates 24 such vignettes, one after the other. The easier the task can be made, the more likely that the respondent will complete the task, and not drop out, as is often the case.

Figure 1. Example of a vignette as it appears on a smartphone.

Most studies with experimental design specify a certain, very limited number of combinations, all tested by many individuals. The objective of the replication is to more accurately estimate the average assigned to the combinations. The sampling of possible combinations is limited, but the ‘strength’ or ‘performance’ of each combination is accurately estimated. Mind Genomics works in a different fashion, more in the spirit of the MRI. Each respondent evaluates a unique combination of 24 vignettes or combinations, like a ‘snapshot’ of the topic. At the same time, since each respondent evaluates a different set of combinations, the Mind Genomics effort estimates the performance of different parts of the underlying ‘space.’ What is missing in the accuracy of one set of estimates provided by replication is more than made up by sampling a great deal more of the space by Mind Genomics.

The respondents were members of the Luc.id panel, comprising 20+ million prospective participants around the world, who had previously agreed to participate in these types of studies. The respondents were specified to be ages 18–30, to live in the United States, with half the panel being male and half the panel being female

Table 2 presents the structure and performance of the first nine vignettes (Vig1-Vig9) for the first respondent, an 18-year old female.  The top part of Table 2 presents the specific combination. The regression program cannot use the data in this form. We ‘expand’ the design to generate 16 variables, one variable corresponding to each element or answer. When a vignette contains the element, the value is ‘1’, else the value is ‘0.’ In this form the regression model can easily analyze the data, to produce a model. Table 2 shows that five vignettes are incomplete. The rationale for this is that only with incomplete or so-called ‘partial profiles’ can the regression analysis return with absolute values for the estimates of the coefficients.

Table 2. The first nine vignettes for respondent #1, showing the combinations, the expanded design for statistical modeling by regression, and the set of dependent variables.

Below the binary expansion we see the ratings.

1. The original ratings were on the anchored 1–9 scale: (1=more unrest, less hope) … (less unrest, more hope)
2. The response time is defined as the number of seconds between the appearance of the vignette on the ‘screen’ and the response. The response time is measured to the nearest tenth of a second.
3. The Top2 is a binary transformation of the original rating data. Ratings of 8–9, close to the high end of the scale (less unrest, more hope) are transformed to 100. The remaining seven scale points, 1–7, are transformed to 0 to denote that they are not positive responses. The ‘not positive’ does not mean negative, but rather means not strongly positive and optimistic.
4. The Bot2 is another binary transformation of the original rating data. Ratings of 1–2, close to the low end of the scale (more unrest, less hope) are transformed to 100. The remaining seven scale points, 3–9, are transformed to 0 to denote that they are not negative responses. Once again, the ‘not negative’ does not mean positive, but rather means not strongly negative and pessimistic.

Selecting the appropriate data to include in the analysis

The Mind Genomics effort creates a great deal of data, since each respondent evaluated 24 vignettes in terms of feelings (optimistic versus pessimistic), and since the response time was also measured. The Mind Genomics program records the response time in tenths of seconds. Typically, the first vignette requires an aberrantly long time, presumably because the respondent does not yet know what to do in terms of where the response key, and so forth. By the time the respondent rates the second vignette, the response times become more stable, and do not show aberrantly high values. All analyses reported here were done without the first vignette from each respondent, and without vignettes whose response time exceeded 10 seconds. Ongoing observations of these studies suggests that when a respondent multi-tasks, the response times are substantially, losing their ability to reflect on underlying behaviors.

Do respondents read the information in the vignettes, or simply gloss over the information? We do not know whether a respondent reads the information in the vignette, pausing to interpret each message, or whether the respondent skips through. Figure 2 shows the distribution of average response times.  The average responses are quite fast, 3 seconds or shorter. Similar types of ‘serious studies’ about public policy have shown longer response times, 5–6 or so, on average. In contrast, ‘fun studies’ about food and shopping generate many short response times.  The many short response times coupled with the fact that the study is about a serious topic suggests that many of the respondents probably skim over the information, and do not absorb it. In contrast, the response times were systematically longer in another study conducted with different respondents, also from Luc.id, this study dealing with the interesting and relevant topic.

Figure 2.   Distribution of average response times across the respondents. The first vignette has been eliminated from the calculation of the average.

About the respondents themselves – pessimistic or optimistic on average?

Our 50 respondents each evaluated 24 vignettes. The initial data transformation created a binary scale for optimism (8–9=100, 1–7=0), and a binary scale for pessimism (1–2=100, 3–9=0). Thus, each respondent generated approximately 23 numbers for optimism, and 23 numbers for pessimism, corresponding to the 23 vignettes considered for analysis

The average of the Top2 for one respondent gives a sense of the percent of the times that the respondent feels optimistic. In turn, the, the average of the Bot2 for one respondent gives a sense of the percent of the times that the respondent feels pessimistic.  The averages can range from (0,0) to (100,0) or (0,100). The (0,0) average means that all the respondent’s ratings are in the middle, neither optimistic nor pessimistic. Averages of optimistic respondents fall near the bottom of the graph in Figure, where the abscissa is high (high percent of optimistic responses), and where the ordinate is low (low percent of pessimistic responses.)  Figure 2 suggests about only about four respondents from the 50 are optimistic.

When we turn the focus to pessimistic respondents, we must look for respondents whose ordinates are high, but whose abscissas are low. These respondents have a large proportion of their responses suggesting pessimism of the outcome. Figure 3 suggests about only two-three respondents can be classified as pessimistic.

Figure 3. Scatterplot of mean Bot2 vs Top2, for respondents, without the first vignette counted. Each circle corresponds to one of the respondents.

The results – what drives a sense of positivity (Top2), a sense of negativity (Bot2), or engages

The real ‘meat’ of a Mind Genomics study emerges when we look at the linkage between the 16 answers or elements, and the response. We created three models using the data from the 47 of the 50  respondents, after removing the first vignette evaluated by each respondent (rationale – learning to answer), and after removing all vignettes requiring more than 9 seconds to evaluate (rationale – respondent probably multi-tasking, so the response time of 30–120 seconds represents the impact of the other task, and not a real measure of the specific vignette which is associated with the very long response time.) Three respondents were over the age of 30, and so we eliminated their analysis because they did not fit the age criteria.  The input data for each regression comprises ALL of the data from the vignettes, the so-called ‘Grand Model.’

Table 3 shows the three columns of data.  We look at the data with the following analytic point of view, based upon hundreds of previous studies.

Table 3. Performance of the elements by total panel.

1. The additive constant: The OLS program estimates the additive constant, and the coefficient for each element. When we create the model with Top2 (Positive Outcome), the additive constant shows the conditional probability of a rating to the vignette being 8 or 9, albeit in the ‘absence of elements.’   In turn, when we create the model with Bot2 (Negative Outcome), the additive constant shows the conditional probability of a rating to the vignette being 1 or 2. We know for a fact that all vignettes comprised a minimum of two and a maximum of four elements, since the design specifies those specific combinations. Thus, the additive constant is an estimated parameter interpreted as ‘what would happen in the absence of elements.’   The additive constants are low for both positive and negative outcomes, 12 and 10, respectively. We interpret the low additive constants as meaning that in the absence of elements, the respondents simply don’t feel that anything will happen. Despite the rhetoric of interested parties, our young respondents don’t feel that anything of significant positive or negative will happen. It’s going to be all in the specifics, if positive or negative outcomes are to happen at all.
2. The strongest positive element (Top2.): Positive, high-scoring elements for Top2, represent the respondent’s belief that the action stated by the element will lead to a positive, peaceful outcome.  We look for elements of 8 or higher, based upon similar types of studies in the past. When an element generates a coefficient of +8 or higher, it often covaries with other things or events in the ‘outside world.’ The number ‘+8’ is not absolute, but rather a convenient level.  Our data in Table 3 from the total panel suggest only one element which even comes near the value, +8. This is element D1, ‘U.S. offers economic development aid to create Middle East Institute of Competitive Excellence.’ That ‘strong performing element,’ at least within the context of this experiment, generates a coefficient of +5.
3. The strongest negative element (Bot2): Positive, high-scoring elements for Bot2, represent the respondent’s belief that the action stated by the element will lead to a negative, combative outcome. Following the same logic as above (#2), we look for an element which generates a coefficient of +8 or higher on Bot2. The only element which does even modestly is B3, Report from U.N. claims that Israeli security forces may have committed war crimes & should be held accountable for the deaths at protests in Gaza last year. The coefficient, however, is only +4.
4. The most engaging elements: The model for response time  (RT) does not have an additive the constant. The rationale is the response time is meaningless without elements. There are no norms from experience for response time, the technology having only been introduced in late 2018. The list below shows those elements requiring 0.9 seconds or longer to ‘process,’ based upon the grand model relating response time to the presence/absence of elements.

   U.S. President Donald Trump say U.S. aid cuts aimed at pressuring the Palestinians to return to peace talks.

   The U.N. General Assembly has a permanent feature on its annual agenda titled “Human rights situation in Palestine & occupied Arab territories”.

   U.S. Jewish organizations reach out to support economic cooperation between Palestine and Israel.

   U.N. condemns the firing of rockets from Gaza into Israeli civilian areas

When we break out the respondents by gender, we find the following:

Optimism (Top2)

1. The additive constant is slightly higher for females than for males (16 versus 10), which at the low end of the scale might signal that females are slightly more optimistic, but neither gender can be labelled ‘optimistic’.
2. In terms of elements, males are very optimistic regarding the prospect of formal efforts to increase economic cooperation and growth, especially the new idea of a Middle East Institute of Competitive Excellence. Surprisingly, females don’t feel optimistic in the face of stated efforts for economic growth.

Pessimism (Bot2)

1. The additive constants are low for both genders, 9 for males and 12 for females, suggesting no real innate pessimism, just like no real innate optimism.
2. No elements drive pessimism

The engaging elements from response time

1. For males a focus on direct efforts to help economics, and the focus on human rights

   U.S. creates Institute to help Palestinians become more entrepreneurial.

   U.S. President Donald Trump say U.S. aid cuts aimed at pressuring the Palestinians to return to peace talks.

   The U.N. General Assembly has a permanent feature on its annual agenda titled “Human rights situation in Palestine & occupied Arab territories”.

2. For females a focus on direct efforts, involving a person (either PM Netanyahu or Pres. Trump)

   Prime Minister Netanyahu said Israel will annex settlements in the West Bank

   U.S. President Donald Trump say U.S. aid cuts aimed at pressuring the Palestinians to return to peace talks.

Uncovering the deeper structure of mind-set and the emergence of two new groups.

A key tenet of Mind Genomics is that for any topic area in which human judgment is involved, there are often different ways of perceiving and judging that which is presented. That is, people are not necessarily uniform in terms of their criteria.  The Latin proverb, here translated, epitomizes the world-view of Mind Genomics: Of taste one does not dispute.

In order to uncover the different viewpoints, or mind-sets, it may be as simple as clustering the respondents based upon the pattern of coefficients, generally without the additive constant.  Clustering is a well-accepted technique in statistics, a procedure to assign items into an exhaustive set of mutually exclusive groups, the aforementioned clusters. These non-overlapping clusters are created according to string mathematical criteria. It is left to the discretion of the researcher to choose the method of clustering and, after the statistics have been calculated, to select the number of clusters and to name them.

Clustering is mathematical, but we are faced with many different ways to cluster a group of test objects, such as people. We must look at clustering as a heuristic, helping us make sense of the data, and not prescribing the absolute truth.  For Mind Genomics, the clustering algorithm as of this writing (2019), comprises the calculation of a distance between pairs of people, putting people in different clusters, and then trying to interpret the meaning of the cluster, if there is a story to be told.

The clustering procedure used here involved the creation of 47 models relating the presence/absence of the 16 to the binary value, Top2, one model or equation for each of the 47 respondents who were under 30. (The other three respondents were excluded entirely from the analysis.). The equation was estimated using all 24 vignettes evaluated by the respondent, because the underlying experimental design is set up to allow the estimation of the individual models when all 24 vignettes are used as cases in the regression model. That is, we included all vignettes, removing no vignette at all.

The OLS regression returned with an additive constant and 16 coefficients for each respondent. We used the 16 coefficients as input to the k-means clustering, defining the distance between any pair of respondents as (1-Pearson R). The Pearson R or correlation coefficient defines the degree of linear relation between two sets of objects.  The k-means clustering put the 47 respondents into two clusters, attempting to maximize the distance between the two centroids (average coefficient for each element by group) and minimize the distance between pairs of respondents within the cluster.

The average coefficients for the two clusters or mind-sets appear in Table 5. The clustering was done on the Top2, the causes for optimism, but the table shows the results for Top2 (optimistic outcome), Bot2 (pessimistic outcome), and response time.  The coefficients are sorted by the strong performers for the two mind-sets.

Table 4. Performance of the elements by gender.

Table 5. Performance of the elements by mind-set.

The two mind-sets are defined by the strongest performing elements:

1. MS1 = Actions of any organized type will bring less unrest, more hope
2. MS2 = Economic development will bring less unrest, more hope

What emerges as very important are those

1. The two mind-sets have low basic optimism
2. Mind-set 1, looking for definitive ‘action’ of any type to bring hope, feels almost nothing will work
3. Mind-set 2, looking for economic development, shows a real hope for simple economic actions, even symbolic but real attempts.

Finding these mind-sets in the population through the PVI (personal viewpoint identifier)

The data suggest little basic optimism or pessimism, whether by gender or even by mind-set.  The level of rhetoric surrounding the situation in the Middle East is not basically relevant.  The low additive constants and the short response times attest to that basic level of disinterest. Yet, despite this discouraging initial finding, the existence of a pair of mind-sets suggests that this disinterest can be turned to more productive interest. Perhaps the first mind-set, those who want action of any sort, cannot be satisfied with destabilizing the region. It is hard to change governments and impossible to change history. The second mind-set, however, is easier to excite. They want concrete symbols and actions to drive economic development. Perhaps, for example, an effort to create this ‘MEICE’ might work, this Middle East Institute of Competitive Excellence, may work.  Author Moskowitz has presented the idea to different countries

(https://www.dropbox.com/s/yhfncfid6b8nmjf/Economic%20Growth%20%20%20Middle%20East%20Institute%20Of%20Competitive%20Excellence.pdf?dl=0.)

Unlike the information about age and gender, people may or may not know the mind-set to which they belong for a specific topic. We know from these data that the mind-sets distribute about equally across genders, and across the self-defined ‘position’ or ‘interest’ in the topic (Table 6.) Furthermore, Table 6 shows us that despite the mind-sets, there is an exceptional level of disinterest.

Table 6. Distribution of the two mind-sets across gender and across self-declared nature of interest in the topic.

The PVI is constructed by considering the elements which best ‘separate’ the two segments. Figure 4 shows the PVI for this study, as well as the feedback which comes to the respondent. The actual PVI as of this writing (April, 2019) resides at the following website:  http://162.243.165.37:3838/TT25/

Figure 4. The PVI for the study of young Americans towards the policy pronouncements about the Middle East, specifically Palestine and Israel.

Discussion and Conclusion

What we learn about the young people

As we noted in the introduction to this paper, one need only look at the media, at the United Nations activities, at the popular pro-Palestinian movements such as BDS (Boycott, Divest, Sanction) SJP (Students for Justice in Palestine) to think that the situation is the Middle East is intractable. For all the efforts to stir up excitement, however, our data suggest that most of the young people whom we sampled simply don’t care. That is, they don’t expect much, neither in the way of peace and hope, nor hostilities and despair.

Yet, despite the apparent disinterest, when we probe deeper through Mind Genomics, we uncover two groups, two different mind-sets. Mind-Set 1 believes that peace and hope will come through definitive action on either side, perhaps believing that the status quo should be maintained. These are the ones who believe in the strength of power, Realpolitik. Mind-Set 2 believes that hope will be nurtured through direct economic help, help which gives to those of both sides in the Middle East an opportunity to grow their nations.  To this end, the notion of a MEICE, the Middle East Institute of Competitive Excellence, makes sense. The structure and activities of that proposed idea are given in a link.

One might dismiss the findings by saying that the data are from a small sample. The reality of Mind Genomics is that it does not measure people per se, but like the science of color, established the basic colors (red, yellow, blue.) All ‘things’ with color comprise a certain percent of the primaries, yellow, red, and blue, adding up to 100%. A colorimeter is used to deconstruct the color of a ‘something’ into the percent of red, yellow, and blue, the primaries. In turn, the colorimeter becomes a tool to ‘measure the world,’ after the color science has been established.  In the same way, our data suggests for this micro-topic the existence of two mind-sets, two ‘primaries,’ those interested in power as a stabilizer, and those interested in economic growth as a stabilizer. One can then use the PVI for this study to understand the feeling of people world-wide. The PVI provides a modern tool to understand the distribution of these mind-sets among the people of the world, because the Mind Genomics effort simply provided the science and the tool. This notion of basic primaries is not new, not original to this paper, having been recognized more than 15 years ago, and undoubtedly far earlier [12,13].

The role of experimentation in counterfactual history

A new and growing area of interest in the social sciences is the discipline of counterfactual history. The Mind Genomics approach we present here for policy fits right in with the notion of creating alternative narratives of what happened or what could happen, presenting these narratives, and getting responses. Most of the efforts are qualitative, teaching people to have a more critical way of thinking. Mind Genomics allows the creation of a systematic body of work for counterfactual history, teaching us both what could have been, and how people react to what could have been.  Perhaps by institutionalizing the teaching of counterfactual history, one might excite an otherwise disinterested generation in understanding the ‘realpolitik’ of today [14].

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DOI: 10.31038/ASMHS.2019323

Abstract

We present a new way to understand how people perceive situations involving other people, situations that could be considered part of the everyday. The approach is Mind Genomics, which assesses the response of people to short, systematically varied vignettes about situations and other people. The responses to these vignettes are deconstructed into the part-worth contribution of the component elements that the vignette comprises, showing the ‘algebra of the mind.’ The deconstruction also is done on response time to the vignettes, showing the ability of the elements to engage attention when the respondent makes a judgment. When Mind Genomics is applied to descriptions of family life under stress, the data suggest that some elements are linked with predicted violence, others are not. Women appear to be more sensitive than men to the individual elements. Three different mind-sets emerged with different perceived ‘triggers’ to predicted family violence, with each mind-set encompassing both men and women: Mind-Set 1 – no specific warning; Mind-Set 2 – Sensitive to the economy; Mind-Set 3 – Family has problems. We present the PVI (personal viewpoint identifier) as a technique to assign new people to these mind-sets.

Introduction

Violence against the other sex, especially in marriage, is not new. Stories of murder and abuse fill the newspapers, the magazines, and the Internet news of today (2019.) Before today’s overwhelming plethora of news, violence by males against females, especially spouses and other family members, occupied a great deal of attention, from those in the news, but of course even more telling, from writers and poets. One cannot read the famous poem, My Last Duchess, by the 19^th Century British poet, Robert Browning without a shudder when one realizes how easy it was to kill one’s spouse. And of course, the popular 1965 Rock n Roll song by Herman’s Hermits, hints at England’s royal lady-killer, King Henry VIII, transformed to a 1960’s idiom of a man with a broken heart. What is popular in literature only reflects what is the common situation in everyday life. The literature in sociology and psychology is replete with studies about violence and anger. Violence against one’s spouse is dealt with in many publications, with the aspects dissected, studied, statistically analyzed and reports issued. Violence seems to be endemic to the relations, starting even in courtship [1]. The spousal violence continues, even into the 60’s [2] Violence emerges when the woman ends up supporting the man [3]. Of course, alcoholism plays a role [4], but so does religion [5] Violence comes from many quarters, but many studies have focused on gender and marriage [6–8].

The foregoing represents just a bit of the available material on violence in the home. These studies focus on both surveys and discussions with individuals. What is lacking is a sense of the richness of the family life through discussion, an absence promoted by the rigidity of the scientific method, but the absence filled by clinicians and social workers. The key issue is to make this topic come alive by merging the rigor of science with the immediacy of storytelling. Violence in the home is especially relevant because it is common, and riveting to those involved. Although there seems to be very little academically-oriented literature recounting the actual ‘story’ of the abuse, the Internet provides a repository of such personal studies in a number of websites, such as:

1. https://www.getdomesticviolencehelp.com/domestic-violence-stories
2. www.hiddenhurt.co.uk/domestic_violence_stories.html
3. https://www.domesticshelters.org/articles/true-survivor-stories

It may be that websites are more conducive to people ‘telling their story’ in their own language. In contrast, the scientific community has made its information almost unobtainable, except to those schooled in the scholastic tradition and able to cut through the jargon and statistics to understand what exactly is happening.

Exploratory Studies through Mind Genomics

This study explores the mind of ‘people’ by having them evaluate different vignettes about violence, vignettes that have been systematically varied, with the components of the vignette, the element, having a richness that is missing from surveys. A review of the scientific literature suggests that many of the studies involving human judgment are done in a manner which is slow, expensive, requiring teams of researchers, and extensive, rigorous statistical analysis. The statistical analysis is often of the type known as ‘inferential, ’ with the objective to confirm or to falsify an ingoing hypothesis, with the hypothesis developed from theory.

Mind Genomics presents to the world of science a different approach, not grounded in theory and confirming or falsifying hypotheses [9]. Rather, Mind Genomics can be liked to an exploration of decisions, using cognitively meaningful stimuli, and dealing with issues of the every day. Mind Genomics can be likened to a new cartographical exercise of a land. Mind Genomics works by presenting vignettes to the respondents, with these vignettes comprising combinations of elements or messages to which a respondent can relate. The respondent reads the vignette and responds to the combination. The research approach is analogous to the MRI, which takes multiple pictures of tissue from different vantage points, and then combines these into a picture of the tissue. The research in this study embodies the Mind Genomics paradigm, dealing with the very important issue of family violence. The objective is to understand a third-party’s estimate of either violence or peace at home occurring when a specific situation is presented, and then to assess the likelihood that each specific element is correlated either with violence or with a peaceful home, respectively, two opposite sides of the scale.

Mind Genomics combines the person with emotion and meaningful description of behavior, i.e., cognitively rich test stimuli. Mind Genomics obtains ratings from the response of people to vignettes about a situation, similar that presented in literature, story-telling, or song. The vignette paints a picture of a situation. The respondent is then asked to judge some aspect of the situation, such as projected violence or projected happiness, based upon what is read. Through this approach it now becomes possible to understand the mind of the person, either the one who is undergoing the experience, or the one who is hearing/reading about the experience. Both points of view differ dramatically from the almost lifeless array of statistics describing a situation. Mind Genomics combines the vividness of experience with numbers, probing the inner mind of the person exposed to the situation, first-hand or second-hand.

The Mind Genomics Approach

The Mind Genomics approach is designed to be exploratory, affordable, iterative, and scalable. This set of objectives in the design means that there are certain simple aspects of the study:

Exploratory: As suggested above, Mind Genomics does not work by confirming or disconfirming a hypothesis extant in the scientific literature. Rather, the exploration means taking new ideas from every-day experience and exploring them to find out the degree to which people respond positively or negatively to them.

Affordable: Mind Genomics is set up to be a so-called DIY, Do it yourself system. The researcher needs access to an APP on the proper machine (Android or Kindle), the ideas (for the researcher), and a convenient source of respondents.

Iterative: Mind Genomics is set up to return the data in easy-to-read formats (PowerPoint® for presentation, Excel® for data analysis. The data return in a matter of a few hours. A new study can be launched a few hours later, after the results from the first study are digested. Furthermore, the results are easy to understand, and set up to promote further exploration with the same tool. With the iterative approach the researcher can do as many as 4–6 studies in a 24-hour period, each study building upon the previous study.

Scalable: Almost anyone can use Mind Genomics to explore problems. The system is scalable across people, but also across different aspects of a topic, by the same researcher. Within a matter of a week or two, the enterprising researcher can conduct 10–20 studies, exploring the different facets of a topic.

Raw Materials

The origin of this study was the focus by author Peer on the causes of violence against women, the fact that so much is known, yet so little. When random people were asked by author Moskowitz about the topic ‘What do you think causes spousal violence, ’ very few people could provide an answer quickly. There was no sense of a well-recognized phenomenon, violence, connected with the daily life of people, other than general statistical compilations, available in the literature. The benefit of a Mind Genomics study is the degree to which it takes any topic and reduces that topic to a set of common aspects, experienced in the everyday. Thus, the elements shown in Table 1 represent the way a person might conceive of the nature of spousal violence. A Mind Genomics is not meant to be exhaustive, but rather introductory, approachable, and in some ways the aforementioned preliminary cartography of the mind, turned to focus on a specific topic. When this notion of ‘cartography’ is recognized and accepted, the position of Mind Genomics advances to a useful, early-stage way of understanding a topic from the mind of people.

Table 1. The raw materials for the study, comprising four questions about the conditions of a family, and the four answers to each question.

The reader will see the approach in (Table 1), showing the four questions (which tell a story), and the four answers to each question. As we read the answers or elements, we should keep in mind that the answers are concrete and simple. When exploring a topic, we can learn a great deal from four simple questions which tell a story, and from the pattern of responses to the 16 answers. The results in this study should reveal a variety of new-to-the-world patterns about domestic violence, based simply on the different ways that people respond to these unambiguous stimuli.

With the inputs shown in Table 1, Mind Genomics creates combinations of answers, so-called vignettes. An example of a vignette appears in (Figure 1).

Figure 1. Example of a vignette as presented to the respondent.

Each respondent evaluated 24 vignettes. The vignettes were constructed according to an experimental design, with the property that a vignette comprised at most one answer from each question, but often had no answers from either one or two of the questions. Thus, the vignettes comprised either two, three, or four answers, the so-called elements. Furthermore, each respondent evaluated a unique set of combinations. The underlying structure of the combinations was maintained, but the specific combinations differed from one respondent to another. To the respondent, the combinations might seem to be random, but the reality is the exact opposite. The experimental design prescribes the combinations. The objective is to present combinations of elements or answers (without the questions), obtain ratings from the respondents who evaluate these combinations, and then deconstruct the ratings into the separate contribution from each element. In this way the respondent is unable to ‘game’ the system by providing politically correct answers. It is virtually impossible to detect the underlying pattern. As a result, the respondent simply relaxes, and gives responses which are more intuitive, and fundamentally less ‘edited.’ In the words of experimental psychologist Daniel Kahneman, the Mind Genomics approach calls into play ‘System 1’ thinking, the fast, almost automatic thinking that we use daily in our lives, when we don’t have to make rational calculations [10].

A sense of the underlying experimental design can be gotten from looking at the schematic in (Table 2), which presents the structure of the first eight vignettes for Respondent #1. The respondent does not, of course, see the underlying structure, but rather the actual combinations, presented on the computer as in Figure 1, or restructured to fit on the screen of a smartphone.

Table 2. Structure of the first eight vignettes for Respondent #1, the conversion to binary for statistical analysis, and the deconstruction of the ratings and response time.

Executing The Study

Each respondent receives the invitation to participate, and is instructed to read the vignette, and to rate it on the 9-point scale.

Here is a set of snapshots of families. Please read the full snapshot and tell us what will happen within the foreseeable future. Read the whole snapshot. Is it going to be peaceful or do you sense some family violence brewing?

What will happen in the foreseeable future with this family? 1 = peace and love … 9 = some violence

The respondent then read each of 24 unique vignettes. The respondent rated vignette on the above 9-point scale. The respondent was then instructed to fill out an open-ended question about violence (results not presented here.) The entire process took approximately 4–5 minutes.

Basic Data Transformation

The experimental design itself must be transformed to a binary no/yes, as shown in Table 2. Only with a binary scale (absent/present) is it feasible to understand the part-worth contribution of every element. In turn, the 9-point scale can be used as a dependent variable, but experience has shown that most people, researchers included, have a difficult time understanding what the scale points mean. Sometimes this difficulty in understand is addressed by labelling each of the nine scale points, a task which itself is fraught with difficulties. An easier way, taken from the world of consumer research, converts the nine-point scale to a binary scale, 0 or 100. Managers find it easy to understand the binary scale and know what to do with a ‘no’ or a ‘yes’ answer. The conventional way to divide the scale creates three regions for the scale; 1–3, 4–6, and 7–9, respectively. Then the following conventions is invoked:

Ratings of 7–9 are assumed to represent ‘violence, ’ and ratings 1–6 are assumed to reflect the lack of violence. For this new variable, ‘violence’, we convert ratings of 1–6 to 0, and ratings of 7–9 to 100. We then add a small random number (<10^–5.) The small random number ensures that that the regression analysis will ‘run’ on the binary-transformed data, even when the respondent confines all of the ratings either to the lower portion of the scale (1–6, transformed to 0), or confines all of the ratings to the upper portion of the scale (7–9 transformed to 100, 1–6 transformed to 0.) The small random number provides just enough variability in the dependent to ensure that the OLS (ordinary least = squares) regression ‘does not crash, ’

Analysis – what drives violence versus happiness – total panel?

The basic analysis in Mind Genomics is OLS (ordinary least-squares) regression, made possible by the ingoing structure of the vignettes for each individual respondent. Every respondent evaluated 24 carefully constructed vignettes, ensuring that at the individual level all 16 elements or answers to the questions, are statistically independent of each other. Most of the vignettes are different from each other, so that the combination of all the vignettes covers a great deal of the ‘design space.’ We combine all the data from the 50 respondents, creating a database of 1200 vignettes (50 x 24 = 1200.) We run two OLS regressions. The first relates the presence/absence of all 16 variables to the binary value of ‘violence’, corresponding to the ratings 7–9 on the original 9-point scale, but now becoming the value 100 on the binary scale for violence. The second OLS regression relates the presence/absence of all 16 variables to the violence of ‘happiness’ corresponding to the ratings of 1–3 on the original 9-point scale.

(Table 3) shows the coefficients for the two equations. The equation is expressed as (Binary Rating) = k₀ + k₁(A1) + k₂(A2) + … k₁₆(D4).

Table 3. Parameters of the model for the Total Panel relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100.)

The additive constant, k₀, is the estimated value of the binary response in the absence of elements. All vignettes comprised a minimum of two and a maximum of four elements. Consequently, the additive constant is an estimated parameter. Nonetheless, the additive constant has value in because it gives a sense of baseline interest or baseline feeling, in the absence of elements. As noted above, the experimental designs ensure that all 16 elements or answers are statistically independent of each other, allowing the absolute coefficients to be estimated. That is, the values of the coefficients are all relative to 0. A coefficient of 10 is twice as high as a coefficient of 5. Furthermore, the transformation of the scale to binary strengthens the mathematic property. The coefficient of 10 means that in the absence of elements, 10% of the responses will be suggest ‘violence’ (7–9). The coefficient of 5 means that in the absence of elements, 5% of the responses, half the number as before, will suggest ‘violence.’ The absolute value of the coefficient means that the coefficients can be compared from study to study, with different topics and different respondents. The ratio scale properties generated by the binary transformation means that one can relate ratio changes in the coefficients (or properly coefficient + additive constant) to external behaviors. The negative coefficient means that when the element is added to the vignette, the percent of response suggesting ‘violence’ will be removed. Thus, when the coefficient is –10, then adding the element to a vignette will decrease the percent suggesting ‘violence’ by 10%. The coefficients are additive and subtractive.

From thousands of such experiments, a set of rules of thumb have emerged about the value of the coefficients, based upon observations of the data, and knowledge about what happens in the external world. The table below provides these guidelines, which are qualitative in nature. There are no fixed values, but rather a shading of importance, so that the higher the positive number the more important the element.

1.	Coefficient of 15 or higher	Extremely important, major signal
2.	Coefficient 8–15	Important to very important
3.	Coefficient of 0–8	From irrelevant to almost important
4.	Coefficient 0 to –6	From irrelevant to almost important
5.	Coefficient from –6 to lower	Important

We interpret the parameters of the model for violence (ratings of 7–9 converted to 100.)

1. The Additive constant is 27, meaning that there is a low likelihood of predicting violence in the absence of elements. We can compare this to say the purchase intent for pizza on the same type of 9-point scale, albeit with different anchors (definitely not buy … definitely buy). The additive constant for pizza is around 60.
2. The elements for predicted violence are low. There is only one which even approaches potential meaningfulness, C4 (The husband is sad and depressed).
3. We move now to the parameters of the model for happiness (ratings of 1–3 converted to 100.)
4. The additive constant is 12, meaning that there is very little in the way of predicted happiness in the absence of elements.
5. Two elements emerge as strong drivers of predicted happiness, both related to season:
   1. B4 (It’s summer time)
   2. B3 (It’s the middle of winter … Christmas)

Genders react differently when predicting violence, but similarly when predicting happiness

Respondents profiled themselves in term of gender. When we divide the data sets by gender and estimate the two models by gender (predicted violence versus predicted happiness), we find dramatic differences in the models for predicted violence, but similar models for predicted happiness (Table 4).

Table 4. Parameters of the model for males versus females relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100.).

Predicted violence

1. Additive constant – lower for females, higher for males (16 vs 39.) The difference suggests that the prediction of violence by female respondent occurs for specific situations. In contrast, for males the additive constant is much higher, suggesting that they predict violence without needing to have specifics.
2. Women predict that the violence will occur in different situations, the most surprising of which is the expectation of violence during Christmas time.

   The husband is sad and depressed

   Companies are firing employees

   It’s in middle of winter … Christmas

Predicted happiness

1. Additive constant is very low, 11 for females, 13 for 13
2. Surprisingly, women are divided on winter and Christmas, with females reacting to

   It’s in the middle of winter … Christmas

   The local economy is growing

   It’s summer time

3. Males are happy as well, with both season and a growing economy

   It’s in the middle of winter … Christmas

   Companies are hiring but people are working long hours

   It’s summer time

When we move from gender to age, we see some dramatic differences as a person goes from older (age 50+) to younger (age 30 to 49, and then age 19 – 29.)

Predicted Violence

1. The additive constants, prediction of violence without other information, are low, with the additive constant lowest for age 50+ (value = 22), and the additive constant modestly higher for age 19 to 29 (value = 31)
2. There are age differences in what drives predicted violence.
3. The oldest respondents, age 50+ predict that violence will occur with the husband sad and depressed, and the companies firing employees.
4. The middle group age predict that violence will occur when the local economy is stressed and in recession
5. The young respondents don’t predict violence will occur in these bad economic times but predict violence will occur when the wife wants to talk but the husband does not.
6. We conclude from this pattern that the older respondents, age 50+, see violence as externally driven, whereas the young respondents, age 19–29 see violence as interaction driven.

Predicted happiness

1. The additive constants, base expectations without elements, vary dramatically across ages. The older respondents (age 50+ and age 30 to 49) see no basic happiness. It’s all a matter of the specifics. The younger respondents, age 19 to 29, in contrast, feel that happiness is all around.
2. The oldest respondents feel that happiness is a function of the time, whether Christmas or the summer.
3. The middle group, age 30 to 39, show some answers which make sense (e.g., companies are honoring, summer time, winter time), but also some answers which don’t make sense (companies are firing employees’ the local economy is stressed and in recession). It could be that this age group feels that the hard times will bring the couple together, rather than eventuate in violence.
4. The youngest group age 19 to 39 feel that happiness will emerge with the Christmas season, but not with the summer season (Table 5)

Table 5. Parameters of the model for the three age groups relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100.).

Response time and engagement with the elements in the vignette

For more than a century, researchers have searched for ‘objective’ correlates of psychological processes. The notion that the information provided by people was not acceptable to many researchers, who believed, whether correctly or not, that only ‘objective’ physical measures could tell the truth about what a person perceives or thinks. The history of these approaches traces back to the original research on reaction time in the Leipzig laboratory of Wilhelm Wundt [11], and moves on to physiological measures of human reactions, whether GSR (galvanic skin response, electrical conductance of the skin), electromyography (muscle currents), then EEG (electroencephalographs and brain waves), culminating in such methods as fMRI [12, 13] There are other more recently introduced methods, such as the implicit association test [14].

Response time, the earliest measure and perhaps the most frequently used measure, may shed additional light on the nature of the way people respond to the elements or answers embedded in the vignettes. Mind Genomics has the distinct benefit that the test stimuli, the elements, are themselves cognitively meaningful. It’s not a case of having to infer ‘what about the stimulus’ makes the respondent process it more quickly or more slowly. One can simply look at the response times to the different elements, using deconstruction method below, and ask whether there is something common about those elements taking longer to process, versus those elements processed more quickly. The Mind Genomics computer program measured the response time to the different vignettes. It then eliminated all vignettes requiring more than 9 seconds to rate, under the assumption that in these Mind Genomics studies, rarely does a respondent stop to consider a vignette for longer than a few seconds. The Mind Genomics program also eliminates all vignettes tested in the first position, with the rationale that at the start of the experiment respondents don’t know what to do.

(Figure 2) shows the distribution of response times, with the abscissa spaced logarithmically. The important thing is the relatively large number of vignettes requiring more than four seconds to process. In many comparable studies, albeit with mundane topics like food, we do not see such long response times. There may be a difference in the way people read serious vignettes, such as the vignettes here, versus ‘fun vignettes’ of other topics.

Figure 2. Distribution of response times for the study on predicted family violence. The distribution has been trimmed to eliminate the responses from the vignette evaluated in the first position, and vignettes registering 9 seconds or longer to evaluate.

The analysis of response times follows the standard approach, involving OLS (ordinary least-squares) regression. The equation is written without the additive constant, based upon the ingoing assumption that in the absence of a vignette with elements, there is no response. All vignettes, however, except those tested first, are included in the OLS regression, with all vignettes of response times 9 or more seconds truncated to 9.

The equation is expressed as: Response Time = k₁(A1) + k₂(A2) … k₁₆(D4)

The analysis was performed in the precisely the same way as the regression analyses for the ratings. That is, the relevant group was identified, and all the appropriate vignettes from everyone in the relevant group was put into a single data file, accessed by the OLS regression package. The coefficients represent the number of tenths of seconds that can be ascribed to each element. The OLS regression deconstructs the response time, estimating the number of tenths of seconds for each element. In the analyses we will look at those response times for individual elements of 1.5 seconds or more. The cut-off of 1.5 seconds is arbitrary, allowing us to get a sense of those elements which strongly engaged the respondents. It is important to keep in mind that these socially-relevant topics appear to be generating longer response times than the more typical business and marketing topics run in the same fashion, with the same type of respondents. It may be that respondents pay more attention to socially relevant topics

The response times for the 16 elements as shown in (Table 6) suggest a continuum with response times of 1.0–1.5 seconds. Keep in mind that all response times over 9 seconds or longer were eliminated as suggesting that the respondent might be doing other things. The data do not suggest a pattern. The most engaging elements, those with the longest response times, talk about the couple, about the economy, and about the woman having problems

Table 6. Response times for the 16 elements, estimated from the data of the Total Panel.

By gender

When we divide the respondents by gender, we see radical differences. The most important result is that men do not find the elements engaging, at least when we operationally define the term ‘engaging’ as a response time of 1.5 seconds (Table 7a).

Table 7a. Response times for the 16 elements, estimated from the data broken out by gender.

Males

The most engaging element is

            The lady is having problems with finances.

The least engaging elements are

            The wife wants to talk but the husband does not

            Companies are firing employees

            It’s summer time

            The family time is shorter together

            The husband is having job troubles

Females

There are many engaging elements. The fact that 8 of the 16 elements are engaging to women suggest that women are simply more attentive than men to the topic of violence versus happiness.

            The husband wants to talk but the wife does not

            The couple are having long term problems

            Companies are hiring but people working long hours

            The lady is having problems with finances

            The local economy is stressed and in recession

            The wife wants to talk but the husband does not

            It’s in middle of winter … Christmas

            Companies are firing employees

Age group

Respondents age 59+

The oldest respondents focus primarily about the issues between the members of the couple, but also react to the economy (companies are hiring but people working long hours.) That element might be a signal for problems that emerge between the husband and wife.

            Companies are hiring but people working long hours

            The husband wants to talk but the wife does not

            The couple are having long term problems

            The wife wants to talk but the husband does not

            The lady is having problems with finances

            It’s in middle of winter … Christmas

            Companies are firing employees

            The lady starts searching for a job to help out

Respondents age 30–49

The most engaging element is the practical issue of finances. The elements are more practical.

            The lady is having problems with finances

            The family all eat at different times

            The local economy is growing

Respondents age –29

None of the elements engaged them. They appear to be disinterested in the topic, or at least don’t pay much attention (Table 7b).

Table 7b. Response times for the 16 elements, estimated from the data broken out by age group.

Mind Sets

One of the key tenets of Mind Genomics is that in any topic area involving judgment and decision-making, there are different groups, mind-sets, showing divergent patterns of what is important. The ideal situation, but one quite rare, is that these mind-sets are congruent with some easy-to-define and measure characteristic or set of characteristics of the respondent. Most of psychological and sociological research discovering groups with different points of view, e.g., voting for political parties, attempt to understand these differences within the framework of the standard ways to divide people. Thus, it is not unusual to see voting patterns broken out by age, gender, market, income, education, work, and so forth. Indeed, the world of analytics attempts to predict these mind-set-driven behaviors from some predictive model using easy to measure variables. In the world of Mind Genomics, the discovery of these basic groups is straightforward, requiring simply one or several studies of the type performed here, and statistical methods to cluster together individuals with similar patterns of coefficients [15] Individuals with similar patterns are assumed to belong to the same ‘mind genome’ for the topic. The creation of the mind genome is a simple statistical analysis, once the relevant experiment has been run. In this respect Mind Genomics holds the advantage of generating easy to interpret ‘mind genomes’ from simple experiments. The reason for the simplicity is that the experiment deals with the topic itself, and the test stimuli are all relevant. One need not array an analytic armory to discover the ‘mind genomes, ’ which emerge readily from these focused experiments.

The procedure for uncovering mind genomes follows these eight steps.

1. Array the vector of all 16 elements for a given respondent as a one line in a data base.
2. Create all the data base, which in our case comprises 16 columns of data (one column per element), and 50 rows (one per respondent).
3. The coefficients tell us the degree to which the respondent would rate that element a 7–9 if the vignette comprised only that element.
4. Apply the method of clustering to divide the set of respondents into two groups, and then again into three groups.
5. Build a model for each of the two groups, and then build a model for each of the three groups.
6. Choose the more parsimonious solution, which is at the same time interpretable.
7. Interpretable means that the strongest positive elements ‘tell a coherent story’.
8. Parsimonious means that the fewer the number of clusters or mind-sets, the better, as long as the mind-sets tell a story which makes sense.

The results from the clustering suggest three mind-sets, as shown in (Table 8). The clustering was done on the coefficients after the ratings were converted to the ‘predicted violence scale’ (ratings of 7–9 converted to 100, ratings of 1–6 converted to 0.) The mind-sets are named according to the elements which generate the highest coefficients for the mind-set.

Table 8. Parameters of the model for the mind-sets relating the presence / absence of the 16 elements to predicted violence (Ratings of 7–9 converted to 100), and to predicted happiness (Ratings of 1–3 converted to 100.) The mind-sets were generated based upon the predicted violence scale.

When we look at response times for the three mind-sets (Table 9) we see dramatic differences in the pattern of elements which ‘engage, ’ i.e., operationally defined as generating a response time of 1.5 seconds or longer. The elements which drive the segmentation also appear to strongly engage only respondents in Mind-Set 3 (family has problems),

Table 9. Response times for the 16 elements, estimated from the separate models, one for each of the three mind-sets.

Mind-Set 3 (Family has problems)

            Companies are hiring but people working long hours

            The couple are having long term problems

            The lady is having problems with finances

            The husband wants to talk but the wife does not

            Companies are firing employees

            It’s in middle of winter are Christmas

Mind-Set 2 (Sensitive to the economy)

            The wife wants to talk but the husband does not

            The family all eat at different times

            The couple are having long term problems

            The local economy is stressed and in recession

            The husband wants to talk but the wife does not

            The local economy is growing

Mind-Set 1 (No specific warning)

            No element engages

The nature of people – optimistic versus pessimistic

The original focus of this paper was the pattern of responses of people to vignettes describing a couple who are in a stressful situation. The pattern of responses of our 50 respondents can also show us whether the respondents themselves are typically optimistic, pessimistic, or neither. The analysis is straightforward. We have 24 samples of the respondent’s evaluations of vignettes, with all elements (answers) appearing an equal number of times, and the basic experimental design structure maintained.

In our preparation for modeling we created binary two scales, each 0/100. Each respondent generates an average on each binary scale. When we look at predicted violence, for example, an average of 100 means that 100% of the time, i.e., for all 24 vignettes, the respondent predicts violence will occur. In contrast, if the average if 50, then the respondent predicts that violence will occur on in half the vignettes.

With this way of plotting the data we can look at the respondents, either one at a time or for key subgroups, to determine where the respondent lies on the scatterplot, and what that implies about the respondent. (Figure 3) shows the scatterplots for total panel, gender, age, and mind-set, respectively.

Figure 3. Scatterplot of binary transformed ratings. Each point is the average binary rating for a respondent. The abscissa is the average for the respondent for ‘predicted violence’ (rating 7–9 converted to 100.) The ordinate is the average for the respondent for ‘predicted happiness’ (rating of 1–3 converted to 100.)

The key things to note are:

1. The 45-degree line means that that the respondent is neither pessimistic nor optimistic but predicts violence and predicts happiness an equal number of times.
2. The further out on the abscissa and the ordinate the respondent falls, the more the respondent is judgmental. There respondent either rates the vignette as describing a situation ending in violence, or describing a situation ending in happiness.
3. The closer the respondent falls to 0, 0 the less frequently the respondent is judgmental.
4. Respondents falling to the right of the line and high on the abscissa (far right) tend to predict violence
5. Respondents falling above the line, and high on the abscissa (far up) tend to predict happiness.
6. Figure 3 immediately shows the greater negativity of females versus males, Age 50+ versus younger respondents, and Mind-Set 2 versus the other two mind-sets, respectively.

Finding the mind-sets in the population (Attila)

The conventional way to discover different groups in the population is through surveys. When one ‘knows’ the subgroup to which a person belongs, e.g., our mind-sets, it is only nature to believe that there are correlates of membership in the population. If only we could discover those correlates, goes the standard plaint. The ingoing assumption is that people who ‘think similarly’ (our mind-sets) should BE similar on the factors used to measure them. An example is age, another is gender, both of which, of course, are surrogates for various life situations and experiences.

(Table 10) suggests that if we are to look to age and to gender as co-variates of segment membership, we are likely to be disappointed. Certainly, as our data suggest, these subgroups exhibit their own general patterns, different from each other, but not suggesting profound differences. In contrast, mind-set segmentation of the type performed with Mind-Genomics data divides people by how they respond, and thus think, in a particular situation.

Table 10. Distribution of the respondents by both the mind-sets (columns) and the more traditional divisions (gender, age, respectively).

The specificity of the mind-set segments to the test stimuli means that we need a way to assign NEW people to one of the three mind-sets. The system must respect the fact that the mind-sets emerged from the elements specific to this topic and this study. Thus, we end up assigning new people to mind-sets based upon a system which is specific to the study. To this end, author Gere has created a PVI, personal viewpoint identifier which uses the pattern of coefficients from the averages for the three segments. The PVI is created by adding ‘noise’ to the basic summary data for the three mind-sets, and then using them to predict mind-set membership. The six strongest predictors in the ‘face of natural noise in data’ are selected as the cohort to be used to assign new people to one of the three mind-sets. (Figure 4) shows the PVI for this study, and the three feedback pages which emerge, depending upon the mind-set to which the respondent is assigned. The feedback pages can be used for further scientific study, for clinical purposes, and even for digital and personal marketing. As of this writing (March, 2019), the PVI is available this website:

Prediction of Violence: Violence: http://162.243.165.37:3838/TT20/

Prediction of Happiness: http://162.243.165.37:3838/TT21/

Figure 4. The PVI (personal viewpoint identifier) for the spousal violence study, by which new people can be assigned to one of the three mind-sets uncovered in the research.

Discussion and Conclusions

This paper presents the emerging science of Mind Genomics as a way to bridge the gap between the impersonal, quantitative dimension of social science and the qualitative, story-telling, emotion-filled and narrative-rich material provided by qualitative methods, story-telling, and literature.

The scientific literature dealing with marital violence provides us with a sense of the many different contributors to the violence in the home, mainly between spouses and but directed to other members of the family. There is a body of sociological and psychological data looking for correlates of family violence. The range of these correlated variables is extensive, as can be sensed from the small sample the literature cited here.

The problem with studying violence and other factors of the ‘human condition’ is the virtual impossibility of doing experiments. The ethics of science and the moral responsibility of people to act ethically precludes doing experiments. We are left with observations and reports. Mind Genomics steps in with an attempt to go one step further, using the ordinary individual as an observer of a reported situation (the experiment), and reacting in terms of a prediction of the outcome (violence, nothing, happiness, respectively.) In this respect we might consider Mind Genomics in these situations to be analogous to the behavioral economics tool of ‘predictive markets, ’ or better ‘information markets’ which uses subjective perceptions embedded in a stock market-like game to drive deep insights into the reasons behind choice [16].

The future holds the promise of learning such as we obtained here, not only for violence in the home, but literally for the many dozens, if not hundreds of life situations that do not permit of an experiment, but may yield some of their secrets to Mind Genomics, which combines the rigor of quantitative science with the richness of cognitively meaningful stimuli actually descriptive of normally lived lives.

Acknowledgment

Attila Gere thanks the support of Premium Postdoctoral Research Program of the Hungarian Academy of Sciences.

The authors wish to thank Dr. Gillie Gabay for her help in formulating the problem and placing it into its academic perspective.

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