Homo Politicus Was Born This Way: How Understanding the Biology of Political Belief Promotes Depolarization

Homo Politicus Was Born This Way: How Understanding the Biology of Political Belief Promotes Depolarization. Alexander Severson, Boise State University. https://static1.squarespace.com/static/5aaee6274eddec9e7a191db5/t/5db34cf7896fba3217a91b4b/1572031736718/Severson+%282019%29.pdf

Abstract: Most individuals perceive ideological beliefs as being freely chosen. Recent research in genopolitics and neuroscience, however, suggests that this conviction is partially unwarranted given that biological and genetic factors explain more variance in political attitudes than choice and environmental factors. Thus, it is worth exploring whether exposure to this research on the biological and genetic basis of political attitudes might influence levels of affective polarization because such exposure might reduce the perceived moral culpability of partisan outgroups
for the endorsement of oppositional beliefs. In this paper, I employ an online survey experiment on Amazon Mechanical Turk (N = 487) to assess whether exposure to research on the genetic and biological etiology of political attitudes influences warmth toward partisan outgroups and preferences over political compromise. I present evidence that nontrivial numbers of participants in the treatment group reject the underlying science and do not update their genetic trait attributions for political attitudes. However, I also find that when the treatment is successful at increasing biological and genetic trait attributions, exposure to this research depolarizes strong-identifying partisans. Moreover, as partisans increasingly endorse biological and genetic trait attributions for political attitudes, they increasingly hold favorable attitudes toward political outgroups. These patterns suggest a potentially profitable inroad for political polarization interventions going forward.

Keywords: polarization; biopolitics; ideology; trait attributions; survey experiment

Exerpts from the introduction:

On June 1, 2019, Democratic primary candidate Andrew Yang tweeted, “According to
twins [sic] studies between one-third and one-half of political alignment is linked to genetics;
that is most of us are born somewhat wired to be liberal or conservative. If this is the case
we need to build bridges as much as possible. It’s not just info or culture” (Yang 2019).
Yang’s tweet is notable as it suggests that one potential strategy to reduce growing partisan
antipathy (Iyengar et al., 2012; Kalmoe and Mason 2019) is to raise public awareness of recent
research in political science which demonstrates that a sizable proportion of individual-level
variation in political attitudes can be explained by biological and genetic factors (Dawes and
Fowler 2008; Hatemi and McDermott 2012). The unstated assumption of this argument is
that it is difficult to hold members of political outgroups responsible for the endorsement
of oppositional political beliefs when variation in such beliefs is best predicted by ascriptive
factors over which individuals have no control. Thus, in this view, awareness of the biological
substrates of political attitudes and of the minimized role of personal choice in generating
those attitudes should increase political tolerance toward partisan outgroups as “born that
way”-style explanations partially absolve members of partisan outgroups of the perceived
evilness of their belief systems (Snead 2011; Schneider, Smith, and Hibbing 2018).

However, it is also imaginable that exposure to information on the biological and genetic sources of political attitudes could further animate partisan tensions. Instead of this
information being used to exculpate members of political outgroups of the perceived offense
of their beliefs, exposure to this information might cause individuals to view the partisan
gulf as elementally unbridgeable. In this perspective, exposure to the degree of determinism
implied by biological models of political attitudes could reduce perceptions that members of
political outgroups are capable of opinion-change. Thus, if the political attitudes of partisan outgroups are viewed as increasingly resistant to change given the biological forces which
underlie them, then it follows that partisans might increasingly disengage from meaningful
social interactions with those across the aisle and come to devalue having conversations with
their outpartisan counterparts. Moreover, belief in the relative fixity of the political attitudes
of partisan outgroups could potentially translate into the adoption of more exaggerated and
essentialist views of the other (Haslam and Whelan 2008).


Conclusion

To summarize, in this paper, I used an online survey experiment to assess whether
exposure to recent scientific findings on the neurobiology and heritability of political belief
influenced affective polarization and preferences over compromise. Theoretically, a priori, it
was unclear whether such a strategy would increase or decrease levels of affective partisan
polarization. On the one hand, a subset of researchers in philosophy and moral psychology
have found that individuals tend to be more forgiving when they perceive that individuals
have less control over their decisions (Young 2009; Baumeister and Brewer 2012; Shariff et al.
2012). Conversely, other researchers in social psychology have found that individuals become
more antisocial when their belief in free-will and choice is undermined (Vohs and Schooler
2008; Baumeister, Masicampo, and DeWall 2009; MacKenzie, Vohs, and Baumeister 2014).
Thus, one of the goals of the present research was to provide a preliminary test of these two
divergent theoretical predictions to assess which, if either, held in the context of the debate
about the degree of determinism of political belief.
In this paper, I present evidence, consistent with recent work by Schneider, Smith, and
Hibbing (2018) and Willoughby et al. (2019), that most people view ideological beliefs and
partisanship as being largely determined by personal choice and to a lesser degree by socialization. Individuals are either unaware of or are psychologically-resistant to the idea that political
beliefs are even partially the byproduct of biological and genetic processes. Further, although
the experimental manipulation increased beliefs that ideology is biologically-determined, the
manipulation was not uniformly effective. However, among those who responded to the manipulation, affective polarization decreased in a rather pronounced fashion, particularly among
strongly-identifying partisans. Moreover, across both conditions, increased endorsement of biological and trait attributions correlated positively with the endorsement of warmer attitudes
toward political outgroups. Finally, my study demonstrated that exposure to such a frame
does not appreciably shift attitudes toward political compromise or whether participants felt
it was important to have ideologically-diverse discussant partners.
However, it is worth noting a few limitations to the present paper which suggest promising avenues for future research. First, the current design cannot rule out the possibility that
any narrative which outsources responsibility for political beliefs to an external locus may
promote depolarization. To this end, future studies should contrast the strength of depolarization effects between frames which emphasize the underlying biological science of ideology
against frames which emphasize the role of socialization factors, frames which would similarly
imply that individuals are not fully-responsible for their own political beliefs. Secondly, in
the present study, I did not directly measure perceptions of the moral culpability or blameworthiness of political outgroups. Future work should investigate whether exposure to frames
which minimize the role of personal choice in the construction of political belief, in turn, alter
perceptions of the moral responsibility of endorsing specific political beliefs. Relatedly, future
work might also explore whether different components of political beliefs (e.g., support for
policies; support for candidates) are perceived as more intentional than others. Third, the
present study made use of a convenience sample conducted on Amazon Mechanical Turk.
While previous work suggests that the use of online convenience samples can recover valid
treatment effect estimates (Mullinix et al. 2015; Coppock 2019), future work could replicate the present findings using a more nationally-representative sample. Finally, although my
results suggest that depolarization interventions which exclusively emphasize the biological
science of ideological belief alone are not likely to engender sweeping depolarizing effects, they
do suggest, perhaps hopefully, that exposure to this type of research neither increases the
partisan affective gulf nor harms the likelihood of cross-party interactions. Thus, concerns
about the potential negative or antisocial effects of encountering such frames, at least in the
context of political belief, may be overstated.
Given that most of us reflexively think that we choose and are responsible for our own
political beliefs, it can be admittedly troubling to confront the possibility that we may not
exercise as much control over these beliefs as our intuition seems to suggest. We proudly
weaponize bumper stickers and traffic in taunt-infused comment-thread witticisms in the war
against the political “other”, all in part because we believe that the other side chooses to
believe what they believe freely and unencumbered. The root of our frustrations, of increased
political violence and partisan discrimination (Lelkes and Westwood 2017), seems to hinge on
this often unquestioned assumption that we exercise agency over our belief systems. However, the emergent neurobiological and genetic science of political belief suggests that this
assumption is misguided and in lieu of accented partisan violence and taunting, potentially
dangerous. It seems odd, albeit perhaps quintessentially human, to believe that our political
beliefs are somehow completely separable from the biological and genetic programming which
circumscribes all of our cognitions. However, in disavowing this belief and accepting that our
own ideologies are partially the byproduct of biological and genetic processes over which we
have no control, we may end up promoting a more tolerant and kinder civil society.

Impact of spatial proximity to a concentration camp 1933-1945 in the 2013 & 2017 German federal elections: Such proximity is associated with a higher vote share of radical-right parties

The long-term impact of the location of concentration camps on radical-right voting in Germany. Julian M. Hoerner, Alexander Jaax, Toni Rodon. December 5, 2019. https://doi.org/10.1177/2053168019891376

Abstract: Of all atrocities committed by state actors in 20th century Europe, the systematic killings by Nazi Germany were arguably the most severe and best documented. While several studies have investigated the impact of the presence of concentration camps on surrounding communities in Germany and the occupied territories in terms of redistribution of wealth and property, the local-level impact on voting behaviour has not yet been explored. We investigated the impact of spatial proximity to a concentration camp between 1933 and 1945 on the likelihood of voting for far-right parties in the 2013 and 2017 federal elections. We find that proximity to a former concentration camp is associated with a higher vote share of such parties. A potential explanation for this finding could be a ‘memory satiation effect’, according to which voters who live in close proximity to former camps and are more frequently confronted with the past are more receptive to revisionist historical accounts questioning the centrality of the Holocaust in the German culture of remembrance.

Keywords: Voting behaviour, long-term effects, far right, Germany, mass violence, culture of memory

Introduction

Of the salient political conflicts that reshaped political competition at the beginning of the 21st century, many are rooted in historical events that lie decades and sometimes centuries in the past. In many cases, these conflicts pit the right to remember past wrongs of territorial or ethnic communities that have been historically marginalized, discriminated and prosecuted against the desire of members of the majority to maintain a particular narrative of a country’s history. However, often these conflicts about how to remember the past also divide society along partisan lines. A substantial body of literature demonstrates that historical events and institutions tend to cast a shadow long after they have ceased to exist, particularly if they involved conflict and violence (Acemoglu et al., 2011; Charnysh and Finkel, 2017).

In this context, we investigated the long-term political impact of the most extreme case of state mass violence – the Holocaust. While any intellectual engagement with the Holocaust should have the victims at its centre, it is also pertinent to analyse its impact on political outcomes in the country responsible for the crimes. We analysed the impact of one of the most visible and prominent symbols of the crimes conducted under the National Socialist dictatorship in Germany: former concentration camps. In particular, we were interested in the impact of living in spatial proximity to a former camp on voting for a far-right party (FRP). Our reasons for choosing this empirical design are twofold: first, physical monuments can be considered a particularly prominent and contentious object of memory, as their presence is visible to everyone in the area and permanent in time (Wüstenberg, 2017). Second, we believe that the impact of the Holocaust on electoral behaviour in Germany deserves particular attention. While there has long been a consensus on German responsibility and the centrality of the Holocaust for German history, this view is now challenged. We thus believe that the German case can tell us a lot about the dynamics of the long-term impact of mass violence and its interaction with political competition in shaping collective memory.

Perhaps surprisingly, we found that the vote share of far-right parties increased as we moved closer to a former concentration camp. Arguably, being repeatedly reminded of an in-group transgression led some voters to be receptive to a revisionist historical narrative that negates the centrality of German guilt. We thus found (indirect) evidence for a ‘political satiation’ effect, in which repeated exposure to cues of in-group responsibility led to higher receptiveness for a revisionist narrative rather than a ‘resilience effect’, in which being reminded of past crimes decreases the likelihood of voting for the far right.

How memory persists: resilience or satiation?

Until now, the largest and most systematic act of state-induced mass violence, the Holocaust, has received rather limited attention by political scientists in terms of its long-term effect on political attitudes and behaviour. One of the few scholarly works focusing specifically on the long-term impact of mass killings in the context of the Holocaust is a recent article by Charnysh and Finkel (2017). The authors analysed the impact on the surrounding communities of the Nazi death camp Treblinka, in Poland, where Germans murdered nearly a million Jews. They show that communities located closer to the camp experienced a property boom, which eventually led these communities to show higher support for an anti-Semitic party, the League of Polish Families. We complement their paper by asking a related question, namely how the crimes of the Nazi dictatorship have impacted on voting behaviour in Germany, the country of the perpetrators.

In so doing, we also hope to contribute to the general literature on far-right voting. This now extensive literature has identified factors such as political opportunity structures (Arzheimer and Carter, 2006), economic grievances such as unemployment (Golder, 2003) and anti-immigrant sentiments (Van der Brug et al., 2005) as determinants of the electoral success of FRPs, even though the interaction between these different factors is complex and multidimensional (Golder, 2016). While there are some studies that focus on the historical antecedents of the success of FRPs, as mentioned above, we aim to provide an original contribution to the literature on far-right voting by focusing on the role of the spatial location of sites of mass violence and the politicization of a country’s culture of memory.

Remembering the Holocaust, the systematic killing of more than 6 million Jewish people and other minorities, has long been considered a defining feature of the raison d’état of the Federal Republic of Germany. The process of remembrance went through several phases. While the initial post-war period was characterized by denial and unwillingness to give a voice to the victims, the student-led revolts of the late 1960s and centre-left governments of the 1970s brought about the preconditions for an active questioning of the past and critical engagement with German guilt (Wüstenberg, 2017: 33). As Art claims, this contestation has given rise to two ‘frames’ of German history: a ‘contrition frame’, focusing on the victims and the responsibility resulting from German guilt, and a ‘normalization frame’, promoted by the right, arguing that discussions of German guilt had to end to allow the country to develop a ‘normal’ national identity (Art, 2005: 10).

Facilities previously serving as concentration camps can be considered one of the most prominent and powerful places of memory relating to the Holocaust. Memorials, places of remembrance or lieux de mémoire are arguably distinct from other forms of memory such as public debates or events in that they are permanent fixtures with which every resident or visitor of the area is confronted (Wüstenberg, 2017: 11). This high visibility makes memorials particularly prone to be subjects of societal mobilization and contestation (Wüstenberg, 2017: 11). We thus hypothesized that spatial proximity to such a lieu de mémoire would have a lasting impact on vote choice in the German context.

We had two distinct hypotheses about the direction of the relationship between living in spatial proximity to a former concentration camp and voting for an FRP. Our first hypothesis was that voters living in close proximity to a former concentration camp would be less likely to vote for such a party. We refer to this as the ‘resilience hypothesis’. In terms of a contemporaneous effect, being constantly reminded of the consequences and extent of German crimes might make voters resilient to any attempts of minimization of German crimes or a ‘normalization frame’. We also believed that there was an additional and related historical mechanism driving such an effect. After the liberation of concentration camps in 1945, the allied powers to varying degrees engaged in denazification measures, mostly carried out at the local level. This experience could have become a shared memory passed down through generations, leading to an aversion to far-right politics and any attempts to qualify or minimize the crimes.

However, revelations about in-group transgressions might also prompt defensive responses and minimization of in-group complicity (Branscombe et al., 2007). We term this the ‘satiation hypothesis’. Satiation as a psychological concept refers to the phenomenon that repeated exposure to a semantic stimulus – in this context embodied by former camps as places of memory – weakens the reaction and receptiveness of a subject to such assertions. Could reactions of defensiveness and minimization of in-group complicity be especially pronounced for those who have received a particularly strong ‘treatment’ of remembrance culture by living close to a former camp? In any case, we would expect both mechanisms to be especially pronounced in – or indeed even limited to – West Germany, as long-ranging debates on how the Holocaust should be remembered were restricted to the Federal Republic of Germany. The German Democratic Republic (GDR) considered itself anti-fascist and thus by definition not responsible for the crimes of the National Socialist dictatorship (Art, 2005: 43). In the next section, we describe our research design to test the resilience and satiation hypotheses empirically.

Happiness is negatively associated with Belief in Luck, but positively associated with Belief in Personal Luckiness

Do the happy-go-lucky? Edmund R. Thompson, Gerard P.  Prendergast, Gerard H. Dericks. Current Psychology, December 6 2019. https://link.springer.com/article/10.1007/s12144-019-00554-w

Abstract: While popular aphorisms and etymologies across diverse languages suggest an intrinsic association between happiness and luck beliefs, empirically testing the existence of any potential link has historically been constrained by varying and unclear conceptualizations of luck beliefs and by their sub-optimally valid measurement. Employing the Thompson and Prendergast Personality and Individual Differences, 54(4), 501-506, (2013) bi-dimensional refinement of trait luck beliefs into, respectively, ‘Belief in Luck’ and ‘Belief in Personal Luckiness’, we explore the relationship between luck beliefs and a range of trait happiness measures. Our analyses (N = 844) find broadly that happiness is negatively associated with Belief in Luck, but positively associated with Belief in Personal Luckiness, although results differ somewhat depending on which measure of happiness is used. We further explore interrelationships between luck beliefs and the five-factor model of personality, finding this latter fully accounts for Belief in Luck’s negative association with happiness, with additional analyses indicating this is wholly attributable to Neuroticism alone: Neuroticism appears to be a possible mediator of Belief in Luck’s negative association with happiness. We additionally find that the five-factor model only partially attenuates Belief in Personal Luckiness’ positive association with happiness, suggesting that Belief in Personal Luckiness may be either a discrete facet of trait happiness or a personality trait in and of itself.

Keywords: Happiness Belief in luck Belief in personal luckiness Five-factor personality model Irrational beliefs

Belief in Luck and Happiness

The Belief in Luck dimension of Thompson and Prendergast’s (2013) bidimensional model distinguishes between, on one hand, luck believers who irrationally consider luck is a deterministic and external phenomenon with agentic qualities capable of influencing outcomes and, on the other, luck disbelievers who consider luck to be merely the product of purely stochastic and uninfluenceable chance. Thompson and Prendergast (2013) found belief or disbelief in luck is not binary, but rather exists on a unidimensional continuum, substantiating Maltby et al.’s (2008) suspicion that the apparently discrete beliefs they found in, respectively, good and bad luck are the product of scoring artifacts rather than separate underlying constructs.

Research to date on Belief in Luck specifically has been scant and limited to inter-item correlations without controls for possible confounding variables. Nonetheless, such correlations hint that believing in luck may be negatively correlated with affect-related measures. For example, Maltby et al. (2008) find belief in luck correlates positively with a range of irrational beliefs and negative traits such as awfulizing and problem avoidance, and Thompson and Prendergast (2013) find it correlates negatively with well-being. Considerable research has demonstrated more generally that irrational beliefs are linked to negative affect (Bridges and Harnish 2010; David and Cramer 2010; David et al. 2002; Kassinove and Eckhardt 1994; Rohsenow and Smith 1982; Smith 1982). Maltby et al. (2008) also find that belief in luck correlates negatively with internal locus of control, while Thompson and Prendergast (2013) find it correlates positively with the powerful others dimension of Levenson’s (1981) locus of control measure. External locus of control, with which belief in luck is commensurate, has long been empirically associated with negative affect (Abramowitz 1969; Buddelmeyer and Powdthavee 2016; Houston 1972; Johnson and Sarason 1978; Yu and Fan 2016). Taken together, these findings are consonant with Maltby et al.’s (2008) suggestion that belief in luck is a facet of irrationality linked to low personal agency, maladaptivity and the negative affect found to be linked with these. Hence it would seem reasonable to suggest that Belief in Luck may be negatively linked with positive dimensions of affect:

• H1. Belief in Luck will be negatively associated with happiness.

Belief in Personal Luckiness and Happiness

Thompson and Prendergast (2013) find both luck believers and disbelievers alike make a subconscious semantic differentiation between luck conceived as a deterministic external phenomenon affecting future events, and luck as a descriptive metaphor for how fortunately past events and current circumstances are believed to have turned out for them personally. Like Maltby et al. (2008), Thompson and Prendergast (2013) find belief in being personally lucky is discrete from and uncorrelated with belief in luck as a deterministic phenomenon. Maltby et al. (2008) find belief in being personally lucky correlates negatively with discomfort-anxiety and with awfulizing, but positively with hope, self-acceptance, positive relations, environmental mastery, and other personality traits associated with positive affect. Similar positive associations between belief in being personally lucky and favorable affective outcomes are reported by Day and Maltby (2003), André (2009), and Jiang et al. (2009). Further mirroring some of Maltby et al.’s (2008) findings, Thompson and Prendergast’s (2013) efforts to establish the nomological validity of the Belief in Personal Luckiness construct find it correlates positively with some affect-related measures, and they speculate it might perhaps constitute a facet of overall well-being. Hence:

• H2. Belief in Personal Luckiness will be positively associated with happiness.
• 
  

Discussion

Luck Beliefs and Happiness

Our finding that Belief in Luck is broadly negatively associated with happiness is consonant with Maltby et al.’s (2008) suggestion that Belief in Luck is perhaps a maladaptive trait. Consequently, any notion of happy-go-lucky individuals cheerfully trusting to luck would seem to be inaccurate, at least if those individuals believe in luck as a non-random, deterministic and external phenomenon. Indeed, insofar as such individuals may irrationally trust to luck as a deterministic phenomenon, they would seem to do so unhappily not happily.

However, our finding that Belief in Personal Luckiness is positively associated with happiness tends to suggest the happy may indeed go lucky, in the sense that happiness and believing oneself to be lucky are associated. Of course, the relatively large size of associations we find here suggests that Belief in Personal Luckiness might in fact be a facet of an overall happiness construct. A possible implication of this is that Belief in Personal Luckiness’ association with any particular happiness measure could, perhaps, be fully accounted for by controlling other happiness measures. To investigate this possibility, we separately regressed each of the four measures of happiness on Belief in Personal Luckiness while simultaneously controlling for the three remaining happiness measures in each respective case, to see if Belief in Personal Luckiness maintained a significant beta. Doing so we found Belief in Personal Luckiness is not associated with either Positive or Negative Affect. However, Belief in Personal Luckiness is still significantly associated with Happiness (β = .09, p < .01; ΔR2 = .05, p < .01), and Optimism (β = .09, p < .01; ΔR2 = .06, p < .01). This would seem to support, partly at least, that Belief in Personal Luckiness may represent either a facet of happiness or a discrete personality trait positively associated with happiness.

Luck Beliefs, Five-Factor Model and Happiness

Neither Belief in Luck nor Belief in Personal Luckiness appear from our findings to be mediators of the association between the five-factor model of personality and happiness.

Indeed, our analyses, in part, suggest the contrary: that Neuroticism fully mediates Belief in Luck’s association with happiness. This does not imply that Belief in Luck necessarily ‘causes’ Neuroticism, but it is reasonable to speculate that the underlying irrationality and the lack of both agency and self-determination that would seem to underpin Belief in Luck also to some extent underpin or are facets of Neuroticism. This would be consonant with previous research demonstrating significant relationships between Neuroticism and locus of control (Judge et al. 2002; Morelli et al. 1979), self-determination (Elliot and Sheldon 1997; Elliot et al. 1997), and irrational beliefs (Davies 2006; Sava 2009).

We do not find evidence for any component of the five-factor personality model mediating Belief in Personal Luckiness’ association with happiness, nor do we find evidence of any pronounced confounding effects between Belief in Personal Luckiness and the five-factor model and their respective associations with happiness. Hence, considering Belief in Personal Luckiness to be a trait discrete from fundamental personality models would on the basis of our findings not seem unreasonable. Nor would it seem unreasonable to suggest that Belief in Personal Luckiness might potentially be either a facet of happiness or a personality trait discrete from but associated with not just the five-factor model but also happiness.

Our conclusions here certainly seem to apply with greatest saliency to the most direct measure of trait happiness we used, Lyubomirsky and Lepper’s (1999) Subjective Happiness Scale, and to a lesser extent to Optimism, a measure closely allied with happiness (Brebner et al. 1995; Chaplin et al. 2010; Furnham and Cheng 2000; Salary and Shaieri 2013). However, while the pattern of relationships is broadly similar for both Positive Affect and Negative Affect, the effect sizes are smaller and either less significant or insignificant. This would suggest that, while both Positive Affect and Negative Affect are often used as proxies for happiness, they might perhaps best be regarded as constructs related to, rather than directly synonyms of, happiness.

Limitations and Further Research

While our research sheds new empirical light on the relationships between luck beliefs, happiness and the five-factor personality model, a number of limitations need to be kept in mind. As with any findings based on cross-sectional data, interpreting our findings in terms of directions of causality would be imprudent and, of course, constrained by the assumption of our research that happiness, luck beliefs, and the five-factor model are all personality traits rather than individual difference states. Personality traits may, of course, be associated in systematic patterns, but the very notion of traits being essentially innate and non-manipulable, unlike individual difference states, intrinsically excludes the possibility that one might be ‘caused’ by another. To take the five-factor model as an example, its five personality traits have a well-established systematic pattern of associations, but it would be implausible to suggest any of the five in any mechanistic sense causes another: they exist together discretely, with none generally argued to be a facet or sub-component or effect of the other. This said, an area for further research might be to examine the effects of trait luck beliefs on state affect that varies temporally and is manipulable, so hence susceptible to theorization and testing using either longitudinal or experimental data.

A further limitation to our study relates to necessary caution in generalizing its findings in view of the deliberately homogeneous population we used. Further research to replicate our findings amongst heterogeneous populations in terms of nationality, occupation, and socio-economic status would be useful as it has been shown across multiple domains that psychological characteristics and their relationships may vary accordingly (Becker et al. 2012; Boyce and Wood 2011; John and Thomsen 2014; Rawwas 2000; Thompson and Phua 2005a, 2005b; Winkelmann and Winkelmann 2008). Furthermore, although each of the happiness and luck measures we employ have been individually validated across internationally diverse samples including Hong Kong Chinese, underlying conceptions of both are known to exhibit nuanced cultural differences (Lu and Gilmour 2004; Lu and Shih 1997; Raphals 2003; Sommer 2007), which conceivably could modify measured associations between them.

We also note that our study, in common with most research, has limitations due to the limited selection of measures with which we operationalized our investigation. We selected just four measures commonly used in studies of trait happiness, but several others exist, although some, like the Satisfaction with Life Scale (Diener et al. 1985) can arguably be regarded as assessing state rather than trait happiness. We also selected a five-factor model measure that, while not as potentially prone to poor measurement validity as extremely short measures, is sufficiently brief as to exclude examination of possible relationships of each of the big-five elements on a sub-component basis. Certainly given our findings in relation to Neuroticism, further research using multi-component measures of this dimension of the five-factor model might prove illuminating.

In addition, research examining possible mediation and moderation effects of cognate psychology constructs such as, for example, locus of control (Pannells and Claxton 2008; Verme 2009), illusion of control (Larson 2008; Erez et al. 1995), and gratitude (Sun and Kong 2013; Toussaint and Friedman 2009) might help further the understanding of relationships between luck beliefs, happiness, and the five-factor model.

Predicting the replicability of social science lab experiments

Altmejd A, Dreber A, Forsell E, Huber J, Imai T, Johannesson M, et al. (2019) Predicting the replicability of social science lab experiments. PLoS ONE 14(12): e0225826. Dec 5 2019. https://doi.org/10.1371/journal.pone.0225826

Abstract: We measure how accurately replication of experimental results can be predicted by black-box statistical models. With data from four large-scale replication projects in experimental psychology and economics, and techniques from machine learning, we train predictive models and study which variables drive predictable replication. The models predicts binary replication with a cross-validated accuracy rate of 70% (AUC of 0.77) and estimates of relative effect sizes with a Spearman ρ of 0.38. The accuracy level is similar to market-aggregated beliefs of peer scientists [1, 2]. The predictive power is validated in a pre-registered out of sample test of the outcome of [3], where 71% (AUC of 0.73) of replications are predicted correctly and effect size correlations amount to ρ = 0.25. Basic features such as the sample and effect sizes in original papers, and whether reported effects are single-variable main effects or two-variable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to those direct replications that are likely to be most informative.

1 Introduction

Replication lies at the heart of the process by which science accumulates knowledge. The ability of other scientists to replicate an experiment or analysis demonstrates robustness, guards against false positives, puts an appropriate burden on scientists to make replication easy for others to do, and can expose the various “researcher degrees of freedom” like p-hacking or forking [4–20].

The most basic type of replication is “direct” replication, which strives to reproduce the creation or analysis of data using methods as close to those used in the original science as possible [21].

Direct replication is difficult and sometimes thankless. It requires the original scientists to be crystal clear about details of their scientific protocol, often demanding extra effort years later. Conducting a replication of other scientists’ work takes time and money, and often has less professional reward than original discovery.

Because direct replication requires scarce scientific resources, it is useful to have methods to evaluate which original findings are likely to replicate robustly or not. Moreover, implicit subjective judgments about replicability are made during many types of science evaluations. Replicability beliefs can be influential when giving advice to granting agencies and foundations on what research deserves funding, when reviewing articles which have been submitted to peer-reviewed journals, during hiring and promotion of colleagues, and in a wide range of informal “post-publication review” processes, whether at large international conferences or small kaffeeklatches.

The process of examining and possibly replicating research is long and complicated. For example, the publication of [22] resulted in a series of replications and subsequent replies [23–26]. The original findings were scrutinized in a thorough and long process that yielded a better understanding of the results and their limitations. Many more published findings would benefit from such examination. The community is in dire need of tools that can make this work more efficient. Statcheck [27] is one such framework that can automatically identify statistical errors in finished papers. In the same vein, we present here a new tool to automatically evaluate the replicability of laboratory experiments in the social sciences.

There are many potential ways to assess whether results will replicate. We propose a simple, black-box, statistical approach, which is deliberately automated in order to require little subjective peer judgment and to minimize costs. This approach leverages the hard work of several recent multi-investigator teams who performed direct replications of experiments in psychology and economics [2, 7, 28, 29]. Based on these actual replications, we fit statistical models to predict replication and analyze which objective features of studies are associated with replicability.

We have 131 direct replications in our dataset. Each can be judged categorically by whether it replicated or not, by a pre-announced binary statistical criterion. The degree of replication can also be judged on a continuous numerical scale, by the size of the effect estimated in the replication compared to the size of the effect in the original study. As binary criterion, we call replications with significant (p ≤ 0.05) effects in the same direction as the original study successful. For the continuous measure, we study the ratio of effect sizes, standardized to correlation coefficients. Our method uses machine learning to predict outcomes and identify the characteristics of study-replication pairs that can best explain the observed replication results [30–33].

We divide the objective features of the original experiment into two classes. The first contains the statistical design properties and outcomes: among these features we have sample size, the effect size and p-value originally measured, and whether a finding is an effect of one variable or an interaction between multiple variables. The second class is the descriptive aspects of the original study which go beyond statistics: these features include how often a published paper has been cited and the number and past success of authors, but also how subjects were compensated. Furthermore, since our model is designed to predict the outcome of specific replication attempts we also include similar properties about the replication that were known beforehand. We also include variables that characterize the difference between the original and replication experiments—such as whether they were conducted in the same country or used the same pool of subjects. See S1 Table for a complete list of variables, and S2 Table for summary statistics.

The statistical and descriptive features are objective. In addition, for a sample of 55 of the study-replication pairs we also have measures of subjective beliefs of peer scientists about how likely a replication attempt was to result in a categorical Yes/No replication, on a 0-100% scale, based on survey responses and prediction market prices [1, 2]. Market participants in these studies predicted replication with an accuracy of 65.5% (assuming that market prices reflect replication probabilities [34] and using a decision threshold of 0.5).

Our proposed model should be seen as a proof-of-concept. It is fitted on an arguably too small data set with an indiscriminately selected feature set. Still, its performance is on par with the predictions of professionals, hinting at a promising future for the use of statistical tools in the evaluation of replicability.