Theory of Change

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Theory_of_Change 

People developing their Theory of Change in a workshop

Theory of Change (ToC) is a methodology or a criterion for planning, participation, adaptive management, and evaluation that is used in companies, philanthropy, not-for-profit, international development, research, and government sectors to promote social change. A Theory of Change of a social program defines its long-term goals and then maps backward to identify necessary preconditions.

Theory of Change explains the process of change by outlining causal linkages in an initiative, i.e., its shorter-term, intermediate, and longer-term outcomes. The identified changes are mapped – as the "outcomes pathway" – showing each outcome in logical relationship to all the others, as well as chronological flow and feedback loops. The links between outcomes are explained by "rationales" or statements of why one outcome is thought to be a prerequisite for another.

The innovation of Theory of Change lies (1) in making the distinction between desired and actual outcomes and (2) in requiring stakeholders to model their desired outcomes before they decide on forms of intervention to achieve those outcomes.

Theory of Change can begin at any stage of an initiative, depending on the intended use. A Theory of Change developed at the outset is best at informing the planning of an initiative. Having worked out a change model, practitioners can make more informed decisions about strategy and tactics. As monitoring and evaluation data become available, stakeholders can periodically refine the Theory of Change as the evidence indicates. A Theory of Change can be developed retrospectively by reading program documents, talking to stakeholders, and analyzing data. This is often done during evaluations reflecting what has worked or not in order to understand the past and plan for the future.

History

Origins

Theory of Change emerged from the field of program theory and program evaluation in the mid 1990s as a new way of analyzing the theories motivating programs and initiatives working for social and political change. Its earlier origins can be traced to Peter Drucker's articulation of Management by Objectives, popularized in his 1954 book The Practice of Management. Management by Objectives requires identifying higher-order Goals, and lower-order Objectives which, if achieved, are expected to result in the Goals being achieved. Theory of Change extends beyond Goals (commonly named Outcomes in Theory of Change terminology) and Objectives to include Impact – the anticipated result of achieving stated goals.

Theory of Change is focused not just on generating knowledge about whether a program is effective, but also on explaining what methods it uses to be effective. Theory of Change as a concept has strong roots in a number of disciplines, including environmental and organizational psychology, but has also increasingly been connected to sociology and political science. Within industrial-organizational psychology, Austin and Bartunek have noted that approaches to organizational development are frequently based on more or less explicit assumptions about 1) the processes through which organizations change, and 2) the interventions needed to effect change.

Development in evaluation practice

Within evaluation practice, Theory of Change emerged in the 1990s at the Aspen Institute Roundtable on Community Change as a means to model and evaluate comprehensive community initiatives. Notable methodologists, such as Huey-tsyh Chen, Peter Rossi, Michael Quinn Patton, Heléne Clark, Carol Taylor Fitz-Gibbon, and Carol Weiss, had been thinking about how to apply program theories to evaluation since the 1970s. The Roundtable's early work focused on working through the challenges of evaluating complex community initiatives. This work culminated in a 1995 publication, ‘New Approaches to Evaluating Comprehensive Community Initiatives’. In that book, Carol Weiss, a member of the Roundtable's steering committee on evaluation, hypothesized that a key reason complex programs are so difficult to evaluate is that the assumptions that inspire them are poorly articulated. She argued that stakeholders of complex community initiatives typically are unclear about how the change process will unfold and therefore place little attention on the early and mid-term changes needed to reach a longer-term goal.

Weiss popularized the term “Theory of Change” as a way to describe the set of assumptions that explain both the mini-steps that lead to the long-term goal of interest and the connections between program activities and outcomes that occur at each step of the way. She challenged designers of complex community-based initiatives to be specific about the theories of change guiding their work and suggested that doing so would improve their overall evaluation plans and would strengthen their ability to claim credit for outcomes that were predicted in their theory. She called for the use of an approach that, at first glance, seems like common sense: lay out the sequence of outcomes that are expected to occur as the result of an intervention, and plan an evaluation strategy around tracking whether these expected outcomes are actually produced. Her stature in the field, and the apparent promise of this idea, motivated a number of foundations to support the use of this technique—later termed “the Theory of Change approach”—in the evaluations of community change initiatives. In the years that followed, a number of evaluations were developed around this approach, fueling more interest in the field about its value and potential application.

Between 2000 – 2002, the Aspen Roundtable for Community Change led the dissemination and case studies of the Theory of Change approach, although still mostly applied to the field of community initiatives. As the Aspen Roundtable concluded its leadership in the field and moved on to apply Theory of Change to such topics as structural racism, others expanded the visibility and application of Theory of Change into international development, public health, human rights and more. The visibility and knowledge of Theory of Change grew with the creation in 2002 of theoryofchange.org and later of Theory of Change Online software.

Growing popularity

In the 2010s, interest increased with some reviews commissioned by Comic Relief in the UK, the Department for International Development in the UK, the Asia Foundation and Oxfam Australia to name a few. The explosion of knowledge of the term, and demand for "theories", led to the formation in 2013 of the first non-profit dedicated to promoting and clarifying standards for Theory of Change. The Center for Theory of Change houses a library, definitions, glossary and is licensed to offer Theory of Change Online by ActKnowledge free of charge.

The use of Theory of Change in planning and evaluation has increased among philanthropies, government agencies, development organizations, universities, international NGOs, the UN, and many other major organizations in both developed and developing countries. This has led to new areas of work, such as linking the Theory of Change approach to systems thinking and complexity. Change processes are no longer seen as linear, but as having many feedback loops that need to be understood. Consequently, Theory of Change is strengthening monitoring, evaluation and learning. They are also helping to understand and assess impact in hard to measure areas, such as governance, capacity strengthening and institutional development. Innovations continue to emerge.

Challenges

Despite the growing ubiquity of Theory of Change, especially in the development arena, understanding of the approach and the methods necessary to implement it effectively are not uniform. In fact, there is evidence of some confusion about what the term ‘Theory of Change’ actually means; in some cases, what some program developers describe as a Theory of Change is, in essence, simply a log frame, strategic plan or another approach that does not encompass the complexity of the Theory of Change approach. There is also inconsistent use of other common Theory of Change terminology (e.g., outputs, outcomes, impacts, etc.), which confounds effective Theory of Change design, evaluation, and learning.

Methodology

Basic structure

A Theory of Change is a high order, or macro, If-Then statement: If this is done, Then these are the anticipated results. The outcomes pathway is a set of needed conditions relevant to a given field of action, which are placed diagrammatically in logical relationship to one another and connected with arrows that posit causality. Outcomes along the pathway are also preconditions to outcomes above them. Thus, early outcomes must be in place for intermediate outcomes to be achieved; intermediate outcomes must be in place for the next set of outcomes to be achieved; and so on. An outcomes pathway therefore represents the change logic and its underlying set of assumptions, which are spelled out in the rationales given for why specific connections exist between outcomes and in the theory narrative.

Quality control criteria

In the early days of Theory of Change, Anne Kubisch and others established three quality control criteria. These are:

Plausibility

Plausibility refers to the logic of the outcomes' pathway. Does it make sense? Are the outcomes in the right order? Are the preconditions each necessary and collectively sufficient to reach the long-term outcomes and ultimate impact? Are there gaps in the logic?

Feasibility

Feasibility refers to whether the initiative can realistically achieve its long-term outcomes and impact. Does the organization have adequate resources? Does it need partners? Does the scope, expectations, or timeline of the theory need adjustment?

Testability

Testability refers chiefly to the indicators: Are they solid and measurable? Will they yield sufficient information to evaluate the success of the initiative? Will they be convincing to necessary audiences?

In addition to these three basic quality control criteria, ActKnowledge has added another key criterion: Appropriate Scope. An actionable theory that can be communicated to the key audiences is dependent in part upon choosing the right scope: broad enough to leave no gaps in the model, yet focused enough on the opportunities and resources at hand. Appropriate Scope also integrates the evaluation concept of “accountability”. Many Theory of Change outcome pathways include an “accountability ceiling,” often a dashed line drawn across the pathway that separates outcomes the organization will monitor and claim credit for attaining from higher-order outcomes that are beyond its power to achieve—e.g., “a just society.”

Applying the model

An Outcomes Pathway mapped out

An important first step in the process is identifying a workable long-term goal and long-term outcomes. The long-term goal should be something the initiative can realistically achieve and that everyone involved understands. A trained external facilitator is best to lead the group to consensus and specificity in this process.

Once a long-term goal is identified, the group then considers: “What conditions must be in place for us to reach the goal?” Any such necessary conditions should be shown as outcomes on the Theory of Change pathway, underneath the long-term outcome. These outcomes act as preconditions to the long-term outcome.

The process of identifying preconditions continues, drilling down the pathway by posing fundamental questions such as: “What has to be in place for this outcome to be achieved?” and “Are these preconditions sufficient for the outcome to be achieved?” In these sessions, participants may use markers, sticky notes, and chart paper to identify and organize outcomes, surface assumptions, develop indicators, and so on.

The messy group work is then usually captured by the facilitator in digital form, through which the content can be expanded, edited, printed, shared, and otherwise managed as the theory continues to be developed.

Theory of change in process and action

Measuring change

The ultimate success of any Theory of Change lies in its ability to demonstrate progress on the achievement of outcomes. Evidence of success confirms the theory and indicates that the initiative is effective. Therefore, the outcomes in a Theory of Change must be coupled with indicators that guide and facilitate measurement.

Indicators may be said to operationalize the outcomes – that is, they make the outcomes understandable in concrete, observable and measurable terms. The relationship of indicator to outcome can be confusing and may be clarified with this simple formula: “I’ll know [outcome reached] when I see [indicator].” For example, “I’ll know that teenagers in the program understand the prenatal nutrition and health guidelines when I see program participants identifying foods that are good sources of nutrition.” A graduated set of indicators can be used to measure and assess the extent to which an outcome has been realized.

Ideally, every outcome on the outcomes pathway (below the dashed accountability ceiling) should have an indicator, but available resources often make that difficult to do. Many groups want to designate priority outcomes – that is, outcomes they know they need to measure if the theory is going to hold. These are the outcomes that must be operationalized (that is, made measurable by one or more indicators.) At a minimum, every outcome for which initial interventions will be designed should have at least one indicator.

Though indicators are valuable, it may be necessary to do more in-depth data collection and analyses to assess whether and how outcomes and assumptions have been realized.

Monitoring and evaluation

As the origins of Theory of Change lie in the field of monitoring and evaluation, developments over the years have ensured that Theory of Change continues to be an invaluable method to conduct evaluations of many different types of projects and organizations. Often posing theory-based evaluation questions helps to focus evaluation efforts on key concerns. As well, there may be a need to pick the right indicators from among the many available, and one can use “monitoring questions” to select the indicators that will be most helpful. The monitoring questions take the form of “What do we really need to know in order to manage grant-making directed to the achievement of this outcome? It is important to understand success beyond just knowing “what works”. Experience has shown that blindly copying or scaling an intervention hardly ever works. An important task for monitoring and evaluation is to gather enough knowledge and understanding so as to be able to predict – with some degree of confidence – how an initiative and set of activities might work in a different situation, or how it needs to be adjusted to get similar or better results. We also need to be able to combine evidence from a number of studies in order to build a stronger picture of what is taking place, how it is unfolding, and, most importantly, how context influences the initiative.

Just as development of a Theory of Change is a participatory process, a ToC-based monitoring and evaluation system can be designed in a participatory way. For example, grant managers can be involved in choosing the outcomes of greatest interest to them in their decision-making. Similarly, people on the ground can have input into which indicators to use and how to operationalize them, choices of instruments and methods of data collection, and which existing sources of data may be used in tracking indicators.

Comparison with other models

Practitioners have developed logic models and logical frameworks as strategies and tools to plan and evaluate social change programs. While these models well articulate the goals and resources of an initiative or organization, they give less focus to the complex social, economic, political and institutional processes that underlie social and societal change. Thus, while logic models and logframes have developed an Implementation Theory behind their work, they can lack an underlying Theory of Change. Theory of Change also contrasts with logic models and logframes by beginning with a participatory process to clearly define desired outcomes and to air and challenge one another's assumptions. Theory of Change can support collective visioning, foster a shared understanding between stakeholders, and bridge thought-styles and different ways of knowing. Theory of Change begins by first working out program goals or desired impact and working backwards on outcome pathways, rather than engaging in conventional forward oriented “so-that” reasoning. As an example of "so-that" reasoning: a grantee decides to increase media coverage on the lack of health insurance among children so that public awareness increases so that policymakers increase their knowledge and interest so that policies change so that more children have health insurance. In Theory of Change, by contrast, the group begins not with its intervention but with its long-term goal and outcomes and then works backward (in time) toward the earliest changes that need to occur. Only when the pathway has been developed is it time to consider which interventions will best produce the outcomes in the pathway.

Many organizations, including the Rockefeller Foundation and the United States Agency for International Development, have used a Results Framework and companion Scorecard as management tools. The Results Framework is complementary and adaptable to a Theory of Change-based monitoring and evaluation system. The framework gives the appearance of being derived from a well-thought-out conceptual model, even if the conceptual model is lacking. The limitations of the Results Frameworks is that they do not show causal connections between conditions that need to change in order to meet the ultimate goals. The added value of Theory of Change lies in revealing the conceptual model, including the causal relationships between and among outcomes, the relationships of activities to outcomes, and of outcomes to indicators. Overall, having a Theory of Change helps make explicit the assumptions upon which the Results Framework is based.

Applications

ToC in International Development

State agencies

• U. K. Department for International Development
• U. S. USAID
• AusAid
• Irish Aid

American foundations

• Rockefeller Foundation
• Ford Foundation

Non-government organizations

• United Nations Development Programme
• World Bank
• Inter-American Development Bank
• World Vision
• The Hunger Project
• CARE
• Amnesty International
• Canadian Feed the Children
• New Philanthropy Capital uses ToC as "a core component of its work" in advising charities and funding organisations, and argues that for funders, "a theory of change helps [them] to articulate the assumptions that underpin" their approach to funding.
• Women for Women International
• Oxfam
• Greenpeace
• Alliance for Financial Inclusion
• California Endowment
• Global Environment Facility, GEF

Research organizations and programs

• CGIAR
• Sustainability Research Effectiveness Program

ToC in Climate Organizations

• The Climate Center

ToC in community schools (U. S.)

• Children's Aid Society
• Communities in Schools (Va.)
• Netter Center for Community Partnerships (Philadelphia)
• Cincinnati Community Learning Centers
• Hartford (Conn.) Community Schools
• Paterson (N. J.) Community Schools
• United Federation of Teachers (New York City)
• Beacon Schools (New York City)

ToC in philanthropy

• Annie E. Casey Foundation
• Kellogg Foundation
• Lumina Foundation for Education

Application in Research Planning, Adaptive Management, and Evaluation

Theory of Change (ToC) is a multi-purpose tool that can be applied for the purpose of planning, managing, monitoring, and evaluating research, especially change-oriented research (e.g., research-for-development, transdisciplinary research, sustainability science). As in other applications, a research ToC describes the causal relationships between a research project or program and its intended results (i.e., outputs, outcomes, and impacts), framed as a set of testable hypotheses about how and why research contributes to change.

ToC for research planning and design

As an ex ante planning tool, Theory of Change can make research design and implementation more realistic and relevant by facilitating critical reflection on the role of research in a change process. It can also make research more participatory, by identifying and engaging key stakeholders of the research, and enabling co-ownership of the research process and the research results (e.g., findings). Theory of Change can accompany or be embedded within a research proposal.

ToC for adaptive management of research

As a monitoring tool, a Theory of Change helps identify useful indicators to assess progress and can facilitate adaptive management. It does this by stimulating learning about what strategies work in the specific research context, and where additional attention and resources need to be directed in order to achieve intended outcomes. This can inform adjustments to planned research activities, and also take advantage of unexpected opportunities by giving users a framework to determine whether an opportunity does or does not align with the purpose and objectives of the research project or program.

ToC for research evaluation

Theory of Change serves as the main analytical framework in theory-based research evaluation. A Theory of Change also helps identify what data are necessary to test whether the change happened as hypothesized. As an ex post evaluation tool, evaluators can assess the actual achievements of research projects or programs against expected outcomes, increasing research transparency and accountability to results.

Innovations

New horizons of theory of change

There are two areas of work that, although not coordinated with Theory of Change, offer much to think about in making Theory of Change more focused and effective:

1. The Annie E. Casey Foundation proposes mapping an organization's social change work along three criteria: Impact, Influence, Leverage.

• The impact of your work is its program outcomes
• Your influence is how much other actors change as a result of your work
• Your leverage is how much investment others put into your model.

To date, Theory of Change has not distinguished impact, influence, and leverage as types of outcomes, but it may be useful to do so as a way of focusing the Theory of Change on measurable achievements. Particularly, when using Theory of Change to guide monitoring and evaluation, the Casey rubric helps focus the group's attention on outcomes, which could, if achieved, be convincingly attributed to the group's work. Other than direct program-related outcomes (impact), the Theory would anticipate outcomes in influence and outcomes in leverage. This approach could thereby help to avoid mapping outcomes involving broad shifts in behavior and values among whole populations, which are easy to think about, but are very difficult to monitor and to attribute to any one program.

2. Another refinement, which directly addresses this problem of attribution, comes from Outcome mapping. This process distinguishes changes in state from changes in behavior, changes in “state” being just those broad shifts in economic conditions, policy, politics, institutional behavior, and so on, among whole populations (e.g., cities, regions, countries, industries, economic sectors, etc.). Measuring changes in state can exceed the capacity of any one actor's monitoring capabilities. Governments collect data on changes in state but, of course, the data may not be calibrated to measure the kinds of change anticipated in any one Theory of Change. Changes in state are also, as stated above, difficult to attribute to any one source.

In contrast, changes in behavior are much easier to monitor, and more easily related to a group's own work. The Outcomes Mapping focus on changes in behavior would tend to direct a Theory of Change toward outcomes like this, which are outcomes the change agent cares most about and which it can relatively easily monitor and evaluate. There would be proportionately less attention to outcomes such as “every child is within five minutes walk of a playground” or “residents are healthy”. Such “changes in state” are more difficult to monitor and to attribute with certainty.

Theory of change and "being strategic"

Does Theory of Change frustrate or complement strategic thinking? This is an ongoing and important discussion, especially as there is an increasing emphasis on and demand for strategy as grounded, flexible, and opportunity-driven. Some perspectives understand ToC as a fixed model that gets in the way of effective work and useful evaluation. However, Patrizi notes that ToC is only at odds with strategic behavior if an organization treats their ToC like any other fixed plan. As Patrizi writes: “Once assumptions [in a theory of change] are laid out, 1) foundations don't actually test those assumptions, and 2) they don't see using the model as a continuous process. It is, like, ‘Well, we did our ToC and now we are done’. If the change model is instead treated as something to adjust as organizations learn what works from experience in the field, then the theory should not be at odds with strategic behavior. If strategy is about seizing opportunities and trying out what works, the Theory of Change model serves as a guiding frame of reference. A list is not a model; a list does not push practitioners to consider the goals as part of a systematic model of change, or to think critically—strategically—about how best to attain the outcomes along the pathway.

Limitations and function of a linear model

Given that things don't happen in a straight-line sequence – as things impact each other in multiple, partly unpredictable ways, with all kinds of feedback loops that aren't modeled in a top-down diagramming format – an important question is: How adequate is the linear Theory of Change model as a description of what's going to happen? One answer to the question is that Theory of Change does not, in fact, model how things happen; rather, it models how we believe things will happen. Theory of Change is a forecast that shows what conditions we believe must exist for other conditions to come into being. As it is forward looking and logical, Theory of Change reflects the way we think logically –that is, if a, then b—and chronologically—first this, then that. The linear format is therefore appropriate. It can be helpful to complement Theory of Change with a process model that shows how the Theory of Change fits into a larger, more cyclical scheme in which theory leads to action, which leads to monitoring and evaluation, which leads to adjustment of the theory, which leads to the next action, more monitoring and evaluation, and so on. Such a process model depicts the linear theory as a conceptual driver of change, which must, to remain useful, be accompanied not only by taking action but also by evaluation and recalibration.

Getting "buy-in" from senior leadership

It is important to remember that it is often a high-level person who has endorsed and initiated the Theory of Change process as something of value, so they have “bought in” at the beginning. The challenge that emerges is, therefore, how to sustain their support while letting others develop the theory. This is similar to many other issues that come up in matters of hierarchy and delegation: effective leaders delegate and trust their teams to carry out the work. There are many variations on the model but usually it involves good measures of delegation and, conversely, of reporting back to get the leader's thinking as the work progresses. It is necessary to come to high-level people having laid out your best thinking: don't go to them without something concrete to which they can respond, but don't wait until everything is perfect, either.

Psychological adaptation

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Psychological_adaptation 

A psychological adaptation seen universally in humans is to easily learn a fear of snakes.

A psychological adaptation is a functional, cognitive or behavioral trait that benefits an organism in its environment. Psychological adaptations fall under the scope of evolved psychological mechanisms (EPMs), however, EPMs refer to a less restricted set. Psychological adaptations include only the functional traits that increase the fitness of an organism, while EPMs refer to any psychological mechanism that developed through the processes of evolution. These additional EPMs are the by-product traits of a species’ evolutionary development (see spandrels), as well as the vestigial traits that no longer benefit the species’ fitness. It can be difficult to tell whether a trait is vestigial or not, so some literature is more lenient and refers to vestigial traits as adaptations, even though they may no longer have adaptive functionality. For example, xenophobic attitudes and behaviors, some have claimed, appear to have certain EPM influences relating to disease aversion, however, in many environments these behaviors will have a detrimental effect on a person's fitness. The principles of psychological adaptation rely on Darwin's theory of evolution and are important to the fields of evolutionary psychology, biology, and cognitive science.

Darwinian theory

Charles Darwin

Charles Darwin proposed his theory of evolution in On the Origin of Species (1859). His theory dictates that adaptations are traits that arise from the selective pressures a species faces in its environment. Adaptations must benefit either an organism's chance of survival or reproduction to be considered adaptive, and are then passed down to the next generation through this process of natural selection. Psychological adaptations are those adaptive traits that we consider cognitive or behavioral. These can include conscious social strategies, subconscious emotional responses (guilt, fear, etc.), or the most innate instincts. Evolutionary psychologists consider a number of factors in what determines a psychological adaptation, such as functionality, complexity, efficiency, and universality. The Adapted Mind is considered a foundational text on evolutionary psychology, further integrating Darwinian theory into modern psychology.

Evolved adaptation vs learned behaviour

An area of disagreement arises between evolutionary psychologists, cognitive scientists and behaviourists on where to draw the line on what is considered a psychological adaptation, and what is considered a learned behaviour. Where behaviourism explains certain behaviours as conditioned responses, cognitivism may push that these behaviours arise from a psychological adaptation that institutes a preference for that behaviour. Evolutionary psychology proposes that the human psychology consists primarily of psychological adaptations, which is opposed by the tabula rasa or blank slate model of human psychology. Early behaviourists, like B.F. Skinner, tended to the blank slate model and argued that innate behaviors and instincts were few, some behaviourists suggesting that the only innate behavior was the ability to learn. On the other hand, Steven Pinker presents the cognitivist perspective in his book, The Blank Slate, in which he challenges the tabula rasa models and argues that human behaviour is shaped by psychological adaptations.

This difference in theory can be seen in research on modern human sexual preferences, with behaviourists arguing that attraction has conditioning influences, such as from the media or cultural norms, while others arguing it is based on psychological adaptations. However, sexual preferences are a difficult subject to test due to the amount of variance and flexibility exhibited in human mate choice. A hybrid resolution to psychological adaptations and learned behaviours refers to an adaptation as the species’ capacity for a certain behavior, while each individual organism still needs to be conditioned to exhibit that behaviour. This approach can explain language acquisition in relation to linguist and cognitive scientist Noam Chomsky's model of human language. His model supports that the capacity for language is a psychological adaptation (involving both the language necessary brain structures and disposition for language acquisition), however, children lack any particular instantiation of language at birth, and must instead learn one in their environment.

Sexual selection

The mating strategies of both sexes can be simplified into different psychological adaptations. There is extensive evidence that incest avoidance, which is the tendency to avoid sexual intercourse with close relatives is an evolved behavioural adaptation. Incest avoidance can be seen cross-culturally in humans, and is evident in wild animals. Evolutionary psychologists argue that incest avoidance adapted due to the greater chance of producing children with severe disabilities when mating with relatives, and because genetic variability offers an increase in fitness regarding offspring survival. Sexual jealousy is another behavior observed in human and non-human animals that appears to be instinctual. Heuristic problem solving and consistent preference for behavioral patterns are considered by some evolutionary psychologists to be psychological adaptations. For example, the tendency for females to change their sexual strategies when faced with developmental pressures such as an absent father may be the result of a psychological adaptation.

Psychological adaptation in males

Human males have developed psychological adaptations, which make them attractive to the opposite sex in order to increase their reproductive success. Evolutionarily, it pays for a male to be polygynous – to have a number of female partners at once – because it means he can create more offspring at once, as they don't have to invest any time in carrying a foetus. Examples of some of these other adaptations include strategies to entice females, strategies to retain a partner and the desire for short-term relationships.

Women find humorous men more attractive.

Humour

It has been researched that humour is sexually selected and acts as a fitness indicator. According to this research it was concluded the production of humour increases mate value in men, and some women seek men with a good sense of humour. In turn, some men are believed to have developed an adaptation in which they endeavour to produce humour with the aim to attract female mates.

Historically, men fight with each other as a mate retention strategy.

Waist-to-hip ratio

Human males have developed an adaptation in which they find women more attractive if they show cues of fertility, such as a good waist–hip ratio. Women with a waist-to-hip ratio of 0.7 are considered more attractive to males than those with a ratio of 0.8, who are considered to have a more masculine figure. This is because they are perceived to be able to have children more and to be more fertile and healthy.

Mate retention

Males have developed behaviours that help them to retain a mate, also known as mate guarding, in order to enhance reproductive success in long-term relationships. Examples are intersexual manipulations which involves the male manipulating the way his partner views their current relationship and to repulse her from other relationships. He could do this by enhancing his own value or decreasing the value of other males. In extreme cases, some men have developed intersexual adaptations that restrict their partner from interacting with other men, including the use of violence. By doing this, women may be less able to leave that relationship, even if it is due to fear. On the other hand, intrasexual manipulations are used to reduce any other options for the women, which could include decreasing their partner's value or make it clear to other males that a woman is 'theirs' by using possessive techniques such as holding her hand in public.

Parental investment

With regards to parental investment, males are much more wary when investing in offspring as they cannot guarantee that the child is theirs. Therefore, as an adaptation, males tend to only invest in offspring if there are high levels of commitment and if they were produced in a long-term relationship as opposed to short-term relationships.

Short-term mating

Some human males have also developed an adaptation in which they have a desire for short-term relationships more than some human females do. This is because men hardly have any investment obligation, whereas a female has to carry a child for nine months if she was to fall pregnant after the sexual encounter. Evolutionarily, it is thought that males have a desire to reproduce as much as they can, and short-term relationships are a good way to inseminate many women with his sperm in order for his genes to continue through generations. There is much evidence for how this short-term mating has evolved psychologically for males, beginning with the desire for a variety of sex partners. It seems that a larger percentage of men, in every culture of the world, desire more than one sex partner in one month compared to women. Furthermore, men are more likely than women to have sexual intercourse with someone having known them for only one hour, one day, one week or one month.

Problems

However, there are some adaptive problems in short-term mating that men must solve; one of these is avoiding commitment and women who might not have sex with the male until they have a signal of commitment or investment. This would reduce the number of partners a male could pursue and succeed with.

Psychological adaptation in females

Female sex-specific adaptations provide evidence of special design for the purpose of increasing fitness and in turn, reproductive success. For example, mate choice, rape aversion tactics and pregnancy sickness are all female-specific psychological adaptations, identified through empirical research, found to increase genetic contributions through survival and reproduction.

Mate-choice as an adaptation

Women can use facial cues such as strong jawlines to detect testosterone presence.

A psychological adaptation for the purpose of reproductive success can be seen in female mate choice. David Buss, an evolutionary psychologist, examines the fundamental principles of selection pressures that create human mate preferences in his contribution to the publication The Adapted Mind. Females have evolved psychological procedures that affect mating decisions in relation to certain male physical attributes and behaviours. Robert Trivers, an evolutionary biologist, outlines the evolutionary basis of these preferences in relation to parental investment and sexual selection. He proposes that females have adapted a preference to mate with males who display both an ability and willingness to invest vital resources for the survival of the female and her offspring. Research suggests females are able to use external cues displayed by males such as territory or physical possessions.

For example, women are able to evaluate the long-term presence of testosterone in men by observing facial testosterone cues. Testosterone stimulates craniofacial development and results in a squarer jaw and consequently, a more masculine appearance. Women in the fertile phase of their menstrual cycle perceive masculine faces as healthier and more attractive than feminine male faces. Females show a psychological adaptation to detect mate quality using these hormonal cues which display the male's fitness and reproductive value. Males who display testosterone cues show a female that they are able to offset the high physiological costs such as immunosuppressant effects.

Rape avoidance

Research proposes that women have evolved psychological mechanisms specifically designed to motivate rape-avoidance behaviours or strategies. This is because rape poses severe costs for the female such as pregnancy, physical harm, injury or death, relationship abandonment and self-esteem depletion. The greatest cost to the female is the circumvention of her mate choice, which threatens reproductive success, resulting in the possession of adaptations in response. Evidence suggests that a number of female-specific traits have evolved in order to reduce the risks associated with experiencing rape. The body-guard hypothesis proposes that rape-avoidance drives women's mate preferences for physically strong or dominant males. Women may also form groups with men and women as a protective alliance against potential rapists. Psychological pain experienced following rape is also identified as an adaptive process designed to focus the female on the social circumstances surrounding the rape for future prevention.

Evidence for this as an adaptation can be seen in reproductive-aged women who are found to experience more psychological pain following rape due to an increased risk of conception. Research also suggests that women in the fertile phase of their menstrual cycle perform fewer risky behaviours that could potentially result in the risk of rape. Women's capacity to resist rape also changes relative to their menstrual cycle; females in the fertile phase show an increase in handgrip strength when placed in a threatening, sexually coercive scenario. Susceptibility to signs of a male's coerciveness is also identified to be better in fertile women.

Pregnancy sickness

One psychological adaptation found solely in women is pregnancy sickness. This is an adaptation resulting from natural selection for the purpose of avoiding toxic-containing foods during pregnancy. Margaret Profet, an evolutionary biologist, provides evidence for this adaptation in a literature review on pregnancy sickness. Particular plant foods, whilst unharmful to adults, can contain toxins (e.g. teratogens) that are dangerous for developing embryos and can potentially cause birth defects such as facial asymmetry. Evidence lies in the finding that women who experience more extreme cases of pregnancy sickness tend to be less likely to miscarry or have babies with birth defects. This fits the criteria for an adaptation as it enhances fitness and increases reproductive success – it results in greater fertility of the mother and contributes to the health of the developing embryo.

Researchers dispute whether this is actually a psychological adaptation, however evidence advocates it is the result of strong selective pressures in our hereditary past. For example, the toxins are found only in natural wild plant foods, not processed foods in our modern-day environment. Furthermore, pregnant women experiencing sickness have been found to avoid particular bitter or pungent smelling foods, potentially containing toxins. Pregnancy induced sickness only typically occurs 3 weeks after conception, around the time when the embryo has started forming major organs and is therefore at the highest risk. It is also a cross-cultural universal adaptation, a suggestion it is an innate mechanism.

Leif Erikson Day

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Leif_Erikson_Day 

 

Leif Erikson Day
U.S. stamp issued on Leif Erikson Day, 1968 (featuring Reykjavík's statue of Leif)
Observed by	United States, parts of Canada, and communities in the Nordic countries
Type	Cultural
Significance	Celebrating Leif Erikson as the first European to lead a voyage to North America
Date	October 9
Next time	October 9, 2024
Frequency	Annual
Related to	Leif Erikson

Leif Erikson Day is an annual observance that occurs on October 9. It honors Leif Erikson (Old Norse: Leifr Eiríksson), the Norse explorer who, in approximately 1000, led the first Europeans believed to have set foot on the continent of North America (other than Greenland).

Because the exact date of Leif's arrival to the Americas is unknown, the October 9 date was chosen in commemoration of the Restauration's arrival to New York Harbor, carrying some of the first Norwegian immigrants to the United States. This means the holiday occurs before Columbus Day (although it is sometimes coincident with the US' observation of Columbus Day).

History

The 1874 book America Not Discovered by Columbus by Norwegian-American Rasmus B. Anderson helped popularize the idea that Vikings were the first Europeans in the New World, an idea that was verified in 1960. In his speech during the Norse-American Centennial at the Minnesota State Fair in 1925, President Calvin Coolidge gave recognition to Leif Erikson as the discoverer of America. In 1929, Wisconsin became the first U.S. state to officially adopt Leif Erikson Day as a state holiday, thanks in large part to efforts by Rasmus Anderson. In 1931, Minnesota did also. As a result of efforts by the Leif Erikson Memorial Association of Saskatchewan, the Legislative Assembly of Saskatchewan proclaimed—through an order-in-council in 1936—that Leif Ericsson Day would be observed on October 9. By 1956, Leif Erikson Day had been made an official observance in seven states (Wisconsin, Minnesota, South Dakota, Illinois, Colorado, Washington, and California) and one Canadian province (Saskatchewan).

The federal government of the United States first recognized Leif Erikson Day in 1935 as a result of House Joint Resolution 26, which had been introduced during the 74th Congress (1935–1936) by Congressman Harry Sauthoff of Wisconsin. Originally, the resolution was written to request the US president annually proclaim October 9 as Leif Erikson Day, but it was amended in committee to be for 1935 only. After passing Congress, the legislation was signed into law by President Franklin D. Roosevelt on June 19, 1935. As requested in the joint resolution, Roosevelt then issued presidential proclamation 2135 on September 11, 1935, designating October 9 of that year as Leif Erikson Day.

Presidential Proclamation 2135 authorized, in 1935, the first US federal observance of Leif Erikson Day. Since 1964, presidential proclamations observing the day have been issued annually.

In the following decades, several unsuccessful attempts were made to pass legislation requesting Leif Erikson Day be proclaimed annually by the president. During the 88th Congress (1963–1964), various members of Congress introduced 12 different resolutions to that effect. One of these pieces of legislation, House Joint Resolution 393 (proposed by Congressman John Blatnik of Minnesota), was passed by Congress and then signed into law by President Lyndon B. Johnson on September 2, 1964, becoming Public Law 88–566. As requested by the joint resolution, President Johnson also signed Presidential Proclamation 3610 proclaiming October 9 of that year as Leif Erikson Day. Under the 1964 joint resolution, each president in the years since has issued an annual proclamation, often using the opportunity also to praise the contributions of Americans of Nordic descent generally and the spirit of discovery.

Bills have been introduced in the Parliament of Canada to observe Leif Erikson Day throughout the country, but they have failed to pass.

Date

October 9 is not associated with any particular event in Leif Erikson's life. The exact date of Leif's arrival to the Americas is unknown, but the Sagas state that it was in autumn. At the suggestion of Christian A. Hoen, October 9 was settled upon, as it took place in the fall and was already a historic date for Scandinavians in America. The date was chosen because the ship Restauration coming from Stavanger, Norway, arrived in New York Harbor on October 9, 1825, beginning a wave of immigration from Norway to the United States.

Observance

The federal government of the United States observes the holiday, and some U.S. states officially commemorate Leif Erikson Day. It is celebrated in many communities, particularly in the Upper Midwest and other places where large numbers of people from the Nordic countries settled. It has long been observed in Seattle, Washington. In 2012, the day was celebrated in Las Vegas, Nevada. Westby, Wisconsin, and Norway, Michigan, have held festivals near the day. There have been Canadian commemorations, including in Edmonton, Alberta, and Charlottetown, Prince Edward Island. The day is also celebrated in Iceland.

In popular culture

The holiday was referenced in the episode "Bubble Buddy" of the Nickelodeon animated series SpongeBob SquarePants. On multiple occasions throughout the episode, characters shout "Happy Leif Erikson Day!", followed by some vaguely Norse-sounding gibberish. It is often written as "hinga dinga durgan". Forbes states that the holiday is often mainly associated online with its appearance in SpongeBob SquarePants and poses that "Perhaps this is the best way to remember the day". The episode is arguably responsible for popularizing the holiday outside of the Norwegian-American community.

Dual inheritance theory

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Dual_inheritance_theory

Dual inheritance theory (DIT), also known as gene–culture coevolution or biocultural evolution, was developed in the 1960s through early 1980s to explain how human behavior is a product of two different and interacting evolutionary processes: genetic evolution and cultural evolution. Genes and culture continually interact in a feedback loop: changes in genes can lead to changes in culture which can then influence genetic selection, and vice versa. One of the theory's central claims is that culture evolves partly through a Darwinian selection process, which dual inheritance theorists often describe by analogy to genetic evolution.

'Culture', in this context is defined as 'socially learned behavior', and 'social learning' is defined as copying behaviors observed in others or acquiring behaviors through being taught by others. Most of the modelling done in the field relies on the first dynamic (copying) though it can be extended to teaching. Social learning at its simplest involves blind copying of behaviors from a model (someone observed behaving), though it is also understood to have many potential biases, including success bias (copying from those who are perceived to be better off), status bias (copying from those with higher status), homophily (copying from those most like ourselves), conformist bias (disproportionately picking up behaviors that more people are performing), etc. Understanding social learning is a system of pattern replication, and understanding that there are different rates of survival for different socially learned cultural variants, this sets up, by definition, an evolutionary structure: cultural evolution.

Because genetic evolution is relatively well understood, most of DIT examines cultural evolution and the interactions between cultural evolution and genetic evolution.

Theoretical basis

DIT holds that genetic and cultural evolution interacted in the evolution of Homo sapiens. DIT recognizes that the natural selection of genotypes is an important component of the evolution of human behavior and that cultural traits can be constrained by genetic imperatives. However, DIT also recognizes that genetic evolution has endowed the human species with a parallel evolutionary process of cultural evolution. DIT makes three main claims:

Culture capacities are adaptations

The human capacity to store and transmit culture arose from genetically evolved psychological mechanisms. This implies that at some point during the evolution of the human species a type of social learning leading to cumulative cultural evolution was evolutionarily advantageous.

Culture evolves

Social learning processes give rise to cultural evolution. Cultural traits are transmitted differently from genetic traits and, therefore, result in different population-level effects on behavioral variation.

Genes and culture co-evolve

Cultural traits alter the social and physical environments under which genetic selection operates. For example, the cultural adoptions of agriculture and dairying have, in humans, caused genetic selection for the traits to digest starch and lactose, respectively. As another example, it is likely that once culture became adaptive, genetic selection caused a refinement of the cognitive architecture that stores and transmits cultural information. This refinement may have further influenced the way culture is stored and the biases that govern its transmission.

DIT also predicts that, under certain situations, cultural evolution may select for traits that are genetically maladaptive. An example of this is the demographic transition, which describes the fall of birth rates within industrialized societies. Dual inheritance theorists hypothesize that the demographic transition may be a result of a prestige bias, where individuals that forgo reproduction to gain more influence in industrial societies are more likely to be chosen as cultural models.

View of culture

People have defined the word "culture" to describe a large set of different phenomena. A definition that sums up what is meant by "culture" in DIT is:

Culture is socially learned information stored in individuals' brains that is capable of affecting behavior.

This view of culture emphasizes population thinking by focusing on the process by which culture is generated and maintained. It also views culture as a dynamic property of individuals, as opposed to a view of culture as a superorganic entity to which individuals must conform. This view's main advantage is that it connects individual-level processes to population-level outcomes.

Genetic influence on cultural evolution

Genes affect cultural evolution via psychological predispositions on cultural learning. Genes encode much of the information needed to form the human brain. Genes constrain the brain's structure and, hence, the ability of the brain to acquire and store culture. Genes may also endow individuals with certain types of transmission bias (described below).

Cultural influences on genetic evolution

Culture can profoundly influence gene frequencies in a population.

Lactase persistence

One of the best known examples is the prevalence of the genotype for adult lactose absorption in human populations, such as Northern Europeans and some African societies, with a long history of raising cattle for milk. Until around 7,500 years ago, lactase production stopped shortly after weaning, and in societies which did not develop dairying, such as East Asians and Amerindians, this is still true today. In areas with lactase persistence, it is believed that by domesticating animals, a source of milk became available while an adult and thus strong selection for lactase persistence could occur, in a Scandinavian population the estimated selection coefficient was 0.09-0.19. This implies that the cultural practice of raising cattle first for meat and later for milk led to selection for genetic traits for lactose digestion. Recently, analysis of natural selection on the human genome suggests that civilization has accelerated genetic change in humans over the past 10,000 years.

Food processing

Culture has driven changes to the human digestive systems making many digestive organs, such as teeth or stomach, smaller than expected for primates of a similar size, and has been attributed to one of the reasons why humans have such large brains compared to other great apes. This is due to food processing. Early examples of food processing include pounding, marinating and most notably cooking. Pounding meat breaks down the muscle fibres, hence taking away some of the job from the mouth, teeth and jaw. Marinating emulates the action of the stomach with high acid levels. Cooking partially breaks down food making it more easily digestible. Food enters the body effectively partly digested, and as such food processing reduces the work that the digestive system has to do. This means that there is selection for smaller digestive organs as the tissue is energetically expensive, those with smaller digestive organs can process their food but at a lower energetic cost than those with larger organs. Cooking is notable because the energy available from food increases when cooked and this also means less time is spent looking for food.

Humans living on cooked diets spend only a fraction of their day chewing compared to other extant primates living on raw diets. American girls and boys spent on average 7 to 8 percent of their day chewing respectively (1.68 to 1.92 hours per day), compared to chimpanzees, who spend more than 6 hours a day chewing. This frees up time which can be used for hunting. A raw diet means hunting is constrained since time spent hunting is time not spent eating and chewing plant material, but cooking reduces the time required to get the day's energy requirements, allowing for more subsistence activities. Digestibility of cooked carbohydrates is approximately on average 30% higher than digestibility of non-cooked carbohydrates. This increased energy intake, more free time and savings made on tissue used in the digestive system allowed for the selection of genes for larger brain size.

Despite its benefits, brain tissue requires a large amount of calories, hence a main constraint in selection for larger brains is calorie intake. A greater calorie intake can support greater quantities of brain tissue. This is argued to explain why human brains can be much larger than other apes, since humans are the only ape to engage in food processing. The cooking of food has influenced genes to the extent that, research suggests, humans cannot live without cooking. A study on 513 individuals consuming long-term raw diets found that as the percentage of their diet which was made up of raw food and/or the length they had been on a diet of raw food increased, their BMI decreased. This is despite access to many non-thermal processing, like grinding, pounding or heating to 48 °C. (118 °F). With approximately 86 billion neurons in the human brain and 60–70 kg body mass, an exclusively raw diet close to that of what extant primates have would be not viable as, when modelled, it is argued that it would require an infeasible level of more than nine hours of feeding every day. However, this is contested, with alternative modelling showing enough calories could be obtained within 5–6 hours per day. Some scientists and anthropologists point to evidence that brain size in the Homo lineage started to increase well before the advent of cooking due to increased consumption of meat and that basic food processing (slicing) accounts for the size reduction in organs related to chewing. Cornélio et al. argues that improving cooperative abilities and a varying of diet to more meat and seeds improved foraging and hunting efficiency. It is this that allowed for the brain expansion, independent of cooking which they argue came much later, a consequence from the complex cognition that developed. Yet this is still an example of a cultural shift in diet and the resulting genetic evolution. Further criticism comes from the controversy of the archaeological evidence available. Some claim there is a lack of evidence of fire control when brain sizes first started expanding. Wrangham argues that anatomical evidence around the time of the origin of Homo erectus (1.8 million years ago), indicates that the control of fire and hence cooking occurred. At this time, the largest reductions in tooth size in the entirety of human evolution occurred, indicating that softer foods became prevalent in the diet. Also at this time was a narrowing of the pelvis indicating a smaller gut and also there is evidence that there was a loss of the ability to climb which Wrangham argues indicates the control of fire, since sleeping on the ground needs fire to ward off predators. The proposed increases in brain size from food processing will have led to a greater mental capacity for further cultural innovation in food processing which will have increased digestive efficiency further providing more energy for further gains in brain size. This positive feedback loop is argued to have led to the rapid brain size increases seen in the Homo lineage.

Mechanisms of cultural evolution

In DIT, the evolution and maintenance of cultures is described by five major mechanisms: natural selection of cultural variants, random variation, cultural drift, guided variation and transmission bias.

Natural selection

Cultural differences among individuals can lead to differential survival of individuals. The patterns of this selective process depend on transmission biases and can result in behavior that is more adaptive to a given environment.

Random variation

Random variation arises from errors in the learning, display or recall of cultural information, and is roughly analogous to the process of mutation in genetic evolution.

Cultural drift

Cultural drift is a process roughly analogous to genetic drift in evolutionary biology. In cultural drift, the frequency of cultural traits in a population may be subject to random fluctuations due to chance variations in which traits are observed and transmitted (sometimes called "sampling error"). These fluctuations might cause cultural variants to disappear from a population. This effect should be especially strong in small populations. A model by Hahn and Bentley shows that cultural drift gives a reasonably good approximation to changes in the popularity of American baby names. Drift processes have also been suggested to explain changes in archaeological pottery and technology patent applications. Changes in the songs of song birds are also thought to arise from drift processes, where distinct dialects in different groups occur due to errors in songbird singing and acquisition by successive generations. Cultural drift is also observed in an early computer model of cultural evolution.

Guided variation

Cultural traits may be gained in a population through the process of individual learning. Once an individual learns a novel trait, it can be transmitted to other members of the population. The process of guided variation depends on an adaptive standard that determines what cultural variants are learned.

Biased transmission

Understanding the different ways that culture traits can be transmitted between individuals has been an important part of DIT research since the 1970s. Transmission biases occur when some cultural variants are favored over others during the process of cultural transmission. Boyd and Richerson (1985) defined and analytically modeled a number of possible transmission biases. The list of biases has been refined over the years, especially by Henrich and McElreath.

Content bias

Content biases result from situations where some aspect of a cultural variant's content makes them more likely to be adopted. Content biases can result from genetic preferences, preferences determined by existing cultural traits, or a combination of the two. For example, food preferences can result from genetic preferences for sugary or fatty foods and socially-learned eating practices and taboos. Content biases are sometimes called "direct biases."

Context bias

Context biases result from individuals using clues about the social structure of their population to determine what cultural variants to adopt. This determination is made without reference to the content of the variant. There are two major categories of context biases: model-based biases, and frequency-dependent biases.

Model-based biases

Model-based biases result when an individual is biased to choose a particular "cultural model" to imitate. There are four major categories of model-based biases: prestige bias, skill bias, success bias, and similarity bias. A "prestige bias" results when individuals are more likely to imitate cultural models that are seen as having more prestige. A measure of prestige could be the amount of deference shown to a potential cultural model by other individuals. A "skill bias" results when individuals can directly observe different cultural models performing a learned skill and are more likely to imitate cultural models that perform better at the specific skill. A "success bias" results from individuals preferentially imitating cultural models that they determine are most generally successful (as opposed to successful at a specific skill as in the skill bias.) A "similarity bias" results when individuals are more likely to imitate cultural models that are perceived as being similar to the individual based on specific traits.

Frequency-dependent biases

Frequency-dependent biases result when an individual is biased to choose particular cultural variants based on their perceived frequency in the population. The most explored frequency-dependent bias is the "conformity bias." Conformity biases result when individuals attempt to copy the mean or the mode cultural variant in the population. Another possible frequency dependent bias is the "rarity bias." The rarity bias results when individuals preferentially choose cultural variants that are less common in the population. The rarity bias is also sometimes called a "nonconformist" or "anti-conformist" bias.

Social learning and cumulative cultural evolution

In DIT, the evolution of culture is dependent on the evolution of social learning. Analytic models show that social learning becomes evolutionarily beneficial when the environment changes with enough frequency that genetic inheritance can not track the changes, but not fast enough that individual learning is more efficient. For environments that have very little variability, social learning is not needed since genes can adapt fast enough to the changes that occur, and innate behaviour is able to deal with the constant environment. In fast changing environments cultural learning would not be useful because what the previous generation knew is now outdated and will provide no benefit in the changed environment, and hence individual learning is more beneficial. It is only in the moderately changing environment where cultural learning becomes useful since each generation shares a mostly similar environment but genes have insufficient time to change to changes in the environment. While other species have social learning, and thus some level of culture, only humans, some birds and chimpanzees are known to have cumulative culture. Boyd and Richerson argue that the evolution of cumulative culture depends on observational learning and is uncommon in other species because it is ineffective when it is rare in a population. They propose that the environmental changes occurring in the Pleistocene may have provided the right environmental conditions. Michael Tomasello argues that cumulative cultural evolution results from a ratchet effect that began when humans developed the cognitive architecture to understand others as mental agents. Furthermore, Tomasello proposed in the 80s that there are some disparities between the observational learning mechanisms found in humans and great apes - which go some way to explain the observable difference between great ape traditions and human types of culture (see Emulation (observational learning)).

Cultural group selection

Further information on cultural group selection: Cultural group selection

Although group selection is commonly thought to be nonexistent or unimportant in genetic evolution, DIT predicts that, due to the nature of cultural inheritance, it may be an important force in cultural evolution. Group selection occurs in cultural evolution because conformist biases make it difficult for novel cultural traits to spread through a population (see above section on transmission biases). Conformist bias also helps maintain variation between groups. These two properties, rare in genetic transmission, are necessary for group selection to operate. Based on an earlier model by Cavalli-Sforza and Feldman, Boyd and Richerson show that conformist biases are almost inevitable when traits spread through social learning, implying that group selection is common in cultural evolution. Analysis of small groups in New Guinea imply that cultural group selection might be a good explanation for slowly changing aspects of social structure, but not for rapidly changing fads. The ability of cultural evolution to maintain intergroup diversity is what allows for the study of cultural phylogenetics.

Historical development

In 1876, Friedrich Engels wrote a manuscript titled The Part Played by Labour in the Transition from Ape to Man, accredited as a founding document of DIH; “The approach to gene-culture coevolution first developed by Engels and developed later on by anthropologists…” is described by Stephen Jay Gould as “…the best nineteenth-century case for gene-culture coevolution.” The idea that human cultures undergo a similar evolutionary process as genetic evolution also goes back to Darwin. In the 1960s, Donald T. Campbell published some of the first theoretical work that adapted principles of evolutionary theory to the evolution of cultures. In 1976, two developments in cultural evolutionary theory set the stage for DIT. In that year Richard Dawkins's The Selfish Gene introduced ideas of cultural evolution to a popular audience. Although one of the best-selling science books of all time, because of its lack of mathematical rigor, it had little effect on the development of DIT. Also in 1976, geneticists Marcus Feldman and Luigi Luca Cavalli-Sforza published the first dynamic models of gene–culture coevolution. These models were to form the basis for subsequent work on DIT, heralded by the publication of three seminal books in the 1980s.

The first was Charles Lumsden and E.O. Wilson's Genes, Mind and Culture. This book outlined a series of mathematical models of how genetic evolution might favor the selection of cultural traits and how cultural traits might, in turn, affect the speed of genetic evolution. While it was the first book published describing how genes and culture might coevolve, it had relatively little effect on the further development of DIT. Some critics felt that their models depended too heavily on genetic mechanisms at the expense of cultural mechanisms. Controversy surrounding Wilson's sociobiological theories may also have decreased the lasting effect of this book.

The second 1981 book was Cavalli-Sforza and Feldman's Cultural Transmission and Evolution: A Quantitative Approach. Borrowing heavily from population genetics and epidemiology, this book built a mathematical theory concerning the spread of cultural traits. It describes the evolutionary implications of vertical transmission, passing cultural traits from parents to offspring; oblique transmission, passing cultural traits from any member of an older generation to a younger generation; and horizontal transmission, passing traits between members of the same population.

The next significant DIT publication was Robert Boyd and Peter Richerson's 1985 Culture and the Evolutionary Process. This book presents the now-standard mathematical models of the evolution of social learning under different environmental conditions, the population effects of social learning, various forces of selection on cultural learning rules, different forms of biased transmission and their population-level effects, and conflicts between cultural and genetic evolution. The book's conclusion also outlined areas for future research that are still relevant today.

Current and future research

In their 1985 book, Boyd and Richerson outlined an agenda for future DIT research. This agenda, outlined below, called for the development of both theoretical models and empirical research. DIT has since built a rich tradition of theoretical models over the past two decades. However, there has not been a comparable level of empirical work.

In a 2006 interview Harvard biologist E. O. Wilson expressed disappointment at the little attention afforded to DIT:

"...for some reason I haven't fully fathomed, this most promising frontier of scientific research has attracted very few people and very little effort."

Kevin Laland and Gillian Ruth Brown attribute this lack of attention to DIT's heavy reliance on formal modeling.

"In many ways the most complex and potentially rewarding of all approaches, [DIT], with its multiple processes and cerebral onslaught of sigmas and deltas, may appear too abstract to all but the most enthusiastic reader. Until such a time as the theoretical hieroglyphics can be translated into a respectable empirical science most observers will remain immune to its message."

Economist Herbert Gintis disagrees with this critique, citing empirical work as well as more recent work using techniques from behavioral economics. These behavioral economic techniques have been adapted to test predictions of cultural evolutionary models in laboratory settings as well as studying differences in cooperation in fifteen small-scale societies in the field.

Since one of the goals of DIT is to explain the distribution of human cultural traits, ethnographic and ethnologic techniques may also be useful for testing hypothesis stemming from DIT. Although findings from traditional ethnologic studies have been used to buttress DIT arguments, thus far there have been little ethnographic fieldwork designed to explicitly test these hypotheses.

Herb Gintis has named DIT one of the two major conceptual theories with potential for unifying the behavioral sciences, including economics, biology, anthropology, sociology, psychology and political science. Because it addresses both the genetic and cultural components of human inheritance, Gintis sees DIT models as providing the best explanations for the ultimate cause of human behavior and the best paradigm for integrating those disciplines with evolutionary theory. In a review of competing evolutionary perspectives on human behavior, Laland and Brown see DIT as the best candidate for uniting the other evolutionary perspectives under one theoretical umbrella.

Relation to other fields

Sociology and cultural anthropology

Two major topics of study in both sociology and cultural anthropology are human cultures and cultural variation. However, Dual Inheritance theorists charge that both disciplines too often treat culture as a static superorganic entity that dictates human behavior. Cultures are defined by a suite of common traits shared by a large group of people. DIT theorists argue that this doesn't sufficiently explain variation in cultural traits at the individual level. By contrast, DIT models human culture at the individual level and views culture as the result of a dynamic evolutionary process at the population level.

Human sociobiology and evolutionary psychology

Evolutionary psychologists study the evolved architecture of the human mind. They see it as composed of many different programs that process information, each with assumptions and procedures that were specialized by natural selection to solve a different adaptive problem faced by our hunter-gatherer ancestors (e.g., choosing mates, hunting, avoiding predators, cooperating, using aggression). These evolved programs contain content-rich assumptions about how the world and other people work. When ideas are passed from mind to mind, they are changed by these evolved inference systems (much like messages get changed in a game of telephone). But the changes are not usually random. Evolved programs add and subtract information, reshaping the ideas in ways that make them more "intuitive", more memorable, and more attention-grabbing. In other words, "memes" (ideas) are not precisely like genes. Genes are normally copied faithfully as they are replicated, but ideas normally are not. It's not just that ideas mutate every once in a while, like genes do. Ideas are transformed every time they are passed from mind to mind, because the sender's message is being interpreted by evolved inference systems in the receiver. It is useful for some applications to note, however, that there are ways to pass ideas which are more resilient and involve substantially less mutation, such as by mass distribution of printed media.

There is no necessary contradiction between evolutionary psychology and DIT, but evolutionary psychologists argue that the psychology implicit in many DIT models is too simple; evolved programs have a rich inferential structure not captured by the idea of a "content bias". They also argue that some of the phenomena DIT models attribute to cultural evolution are cases of "evoked culture"—situations in which different evolved programs are activated in different places, in response to cues in the environment.

Sociobiologists try to understand how maximizing genetic fitness, in either the modern era or past environments, can explain human behavior. When faced with a trait that seems maladaptive, some sociobiologists try to determine how the trait actually increases genetic fitness (maybe through kin selection or by speculating about early evolutionary environments). Dual inheritance theorists, in contrast, will consider a variety of genetic and cultural processes in addition to natural selection on genes.

Human behavioral ecology

Human behavioral ecology (HBE) and DIT have a similar relationship to what ecology and evolutionary biology have in the biological sciences. HBE is more concerned about ecological process and DIT more focused on historical process. One difference is that human behavioral ecologists often assume that culture is a system that produces the most adaptive outcome in a given environment. This implies that similar behavioral traditions should be found in similar environments. However, this is not always the case. A study of African cultures showed that cultural history was a better predictor of cultural traits than local ecological conditions.

Memetics

Memetics, which comes from the meme idea described in Dawkins's The Selfish Gene, is similar to DIT in that it treats culture as an evolutionary process that is distinct from genetic transmission. However, there are some philosophical differences between memetics and DIT. One difference is that memetics' focus is on the selection potential of discrete replicators (memes), where DIT allows for transmission of both non-replicators and non-discrete cultural variants. DIT does not assume that replicators are necessary for cumulative adaptive evolution. DIT also more strongly emphasizes the role of genetic inheritance in shaping the capacity for cultural evolution. But perhaps the biggest difference is a difference in academic lineage. Memetics as a label is more influential in popular culture than in academia. Critics of memetics argue that it is lacking in empirical support or is conceptually ill-founded, and question whether there is hope for the memetic research program succeeding. Proponents point out that many cultural traits are discrete, and that many existing models of cultural inheritance assume discrete cultural units, and hence involve memes.

Shortcomings and criticisms

Psychologist Liane Gabora has criticised DIT. She argues that use of the term ‘dual inheritance’ to refer to not just traits that are transmitted by way of a self-assembly code (as in genetic evolution) but also traits that are not transmitted by way of a self-assembly code (as in cultural evolution) is misleading, because this second use does not capture the algorithmic structure that makes an inheritance system require a particular kind of mathematical framework.

Other criticisms of the effort to frame culture in Darwinian terms have been leveled by Richard Lewontin, Niles Eldredge, and Stuart Kauffman.