Everything You Need To Know To Find The Best Simulation Primitive Humans

Abstract

Analyses of human evolution are fundamental to understand the current gradients of human diversity. In this concern, genetic samples collected from current populations together with archaeological data are the most important resources to study human evolution. However, they are often insufficient to properly evaluate a variety of evolutionary scenarios, leading to continuous debates and discussions. A commonly applied strategy consists of the use of computer simulations based on, as realistic as possible, evolutionary models, to evaluate alternative evolutionary scenarios through statistical correlations with the real data. Computer simulations can also be applied to estimate evolutionary parameters or to study the role of each parameter on the evolutionary process. Here we review the mainly used methods and evolutionary frameworks to perform realistic spatially explicit computer simulations of human evolution. Although we focus on human evolution, most of the methods and software we describe can also be used to study other species. We also describe the importance of considering spatially explicit models to better mimic human evolutionary scenarios based on a variety of phenomena such as range expansions, range shifts, range contractions, sex-biased dispersal, long-distance dispersal or admixtures of populations. We finally discuss future implementations to improve current spatially explicit simulations and their derived applications in human evolution.

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Keywords: Demographic models, Human evolution, Human landscape genetics, Molecular evolution, Range expansion, Spatially explicit simulation.

INTRODUCTION

The evolutionary history of humans has been largely studied in order to shed light on where and when the first humans colonized the world and how such a colonization took place. Indeed, knowledge about current human genetic variation may help to understand human diseases, for example those presenting variable behaviour among ethnic groups [e.g., 1-3]. Fortunately, genetic signatures from past human evolutionary processes are still present in current humans, and together with archaeological records, may allow us to study human evolution. However, the interpretation of such genetic signatures (e.g., assign a genetic feature to a particular ancestral event) is not straightforward. For instance, different ancestral events might produce a similar genetic effect or a combination of events might lead to complex genetic information. These uncertainties can be especially noted in the literature of human evolution by continuous discussions [e.g., 4, 5]. Below we briefly describe some interesting current topics of debate:

• Geographic origin of modern humans. The origins of the modern Homo sapiens remain unclear. It is widely assumed that modern humans originated in Central or South Africa, which is indeed supported by archeological data [e.g., 6-8]. However, other geographic origins have been proposed, for instance, North Africa [e.g., 9, 10] and even multiregional origins through a worldwide gradual transition from earlier humans [11, 12].

• Geographic out-of-Africa migration routes. Another interesting topic is the out-of-Africa migration corridors from where modern humans started the colonization of the world at approximately 125-100 kya [5, 13]. Here there are two main routes under discussion. The first one is the traditionally considered route through the Nile Valley and the North of present Egypt [e.g., 14, 15]. The second route is through the Bab-el-Mandeb Strait towards present South Arabia [e.g., 16, 17], whose sea level may have been much lower at the time of the migration [13]. Of course, another possibility is the consideration of both migration routes [e.g., 18, 19].

• Principal component analysis of European human genetic diversity gradients. The colonization of Europe by modern humans was initially studied by Cavalli-Sforza et al. [20, 21]. They proposed a demic diffusion (DD) scenario based on a progressive introgression of genes from the local populations (hunter-gatherers, Paleolithic) to the invading populations (farmers, Neolithic) that may have generated a gradient of allele frequencies along the expanding axis [21-23]. Cavalli-Sforza et al. represented these gradients by using principal component analysis (PCA) and interpreted the resulting principal components (PCs) as past migration events [20, 21]. Nevertheless, there is a controversy in this interpretation because the PCs may have arisen from isolation-by-distance scenarios at equilibrium, without requiring any expansion [4, 24, 25].

• Colonization of the Americas. It is accepted that the Americas were colonized through several waves beginning 16.5 kya, by crossing the present Bering Strait [26-28], which could be transited at that time as a consequence of the last glacial maximum (LGM) [28, 29]. A proposed scenario considers an initial Pacific coastline migration due to the impediment of Canadian ice sheets that were formed during the LGM [28]. This scenario could explain the early Colombian settlement found by Hellenthal et al. [30] where independent sources of ancestry for Northern and Southern Americans are suggested. By contrast, other studies propose a series of waves where the Americas were colonized from North to South and therefore, the ancestry of South American inhabitants can be related to North America ancestral populations [e.g., 26, 31].

• Admixture of Human populations. The admixture of different ancestral human populations is an interesting topic of debate. There is some evidence of admixture between Paleolithic and archaic humans (0.5-2.1%) [32-34] but the genomic distributions of such an admixture are still lacking [see for a review, 35]. On the other hand, the amount of admixture between Paleolithic and Neolithic populations is highly debated and current estimates are described between 20 and 80% depending on the applied methods and data [see e.g. 36-40].

A strategy to help with the above debates consists of the application of computer simulations. In general, computer simulations aim to mimic real world processes and present a variety of applications [see the reviews, 41-45]. Simulations allow for the study of evolutionary aspects that may alter entire processes or enable the understanding of complex systems that are analytically intractable [46]. As noted in [41], computer simulations are widely applied in population genetics for hypothesis testing [e.g., 47-50], to validate and compare analytical frameworks [e.g., 51, 52], to study interactions among evolutionary forces [e.g., 49, 53], or to estimate evolutionary parameters [e.g., 54, 55]. The choice of an appropriate simulator is fundamental because generally simulations should be as realistic as possible to mimic real-world scenarios of population genetics [56-58]. Computer simulations using spatially explicit models can be useful to analyze the influence of habitat on organism evolution at different spatial and temporal scales [59]. In human evolution, spatially explicit simulations have provided important advances to the current understanding of genetic diversity through the estimation of evolutionary parameters and through hypothesis testing of alternative evolutionary models.

This study provides an overview of spatially explicit models and the derived simulation frameworks that are commonly applied to study human evolution. The implemented evolutionary scenarios, with their associated advantages and limitations, are discussed. Then, we describe a variety of human evolution studies based on spatially explicit computer simulations. Finally, we conclude with a discussion on the importance of considering more rational evolutionary scenarios that would help to simulate a more realistic human evolution and generate more accurate inferences. We also discuss the direct incorporation of spatially explicit simulations on analytical methods like the approximate Bayesian computation approach.

SPATIAL AND TEMPORAL SIMULATIONS

Two main approaches are commonly used in population and landscape genetics to simulate evolutionary histories, the coalescent (backward-time) and the forward in time (forward-time). The latter approach includes the spatially explicit models. Below we describe briefly the main particularities of both approaches.

Coalescent Simulations

The coalescent [60] describes the genealogical history of a sample of alleles from the present to a single ancestral copy [see reviews, 61, 62]. Interestingly, it only simulates the backwards in time evolution of a sample and therefore, coalescent simulations are frequently computationally faster than other methods based on the evolution of the whole population (see later). Currently, the coalescent can only simulate a few population genetics models such as demographics [e.g., 63], population history and migration [e.g., 64, 65], gene flow and recombination [e.g., 66, 67] and selection [e.g., 68, 69]. Coalescent simulations can be used in human evolutionary studies [e.g., 50, 70]. In fact, the coalescent is especially interesting when extensive simulations are required (i.e., in analyses based on the approximate Bayesian computation approach) [e.g., 50, 70]. Nevertheless, forward-time simulations can be much more realistic to mimic human evolution due to the consideration of a wide variety of evolutionary processes (see following subsections).

Forward-time Simulations

The forward-time approach evolves the whole population from the past to the present [see reviews, 45, 71, 72]. As a consequence, this approach considers all the ancestral information of the population allowing for individual-individual interactions [e.g., 73], admixture of populations [e.g., 74], complex selection [e.g., 73, 75, 76] and complex migration models [e.g., 74, 76, 77]. Nevertheless, forward simulations are computationally slower than coalescent simulations due to the simulation of the entire population, although recent methods showed improvements in this concern [e.g., 78]. Interestingly, two recent simulators have combined both coalescent and forward-time approaches allowing fast simulation under some complex evolutionary scenarios [79, 80].

Spatially Explicit Simulations

The forward approach includes temporal and spatial (1-dimensional, 2D, or 3D)) models. In terrestrial animals like humans, it is known that 2D spatially explicit models may generate more realistic simulations than models with a lower number of dimensions [45, 59]. This improvement is probably due to the consideration of spatial constraints such as population range expansions [see for a review, 58, 81] or environmental changes [e.g., 49, 53, 58, 82]. Overall, spatially explicit models can better consider the available information and provide more realistic explanations for the observations.

The main goal of spatially explicit models is to combine demographic and genetic processes with a given landscape map, where the landscape features may influence the evolution of the population. Real-world maps can be imported from a Geographical Information System (GIS) tool that usually can also split the map into a lattice of demes by defining a deme size [e.g., 83]. Initially, a deme is chosen to start the colonization, and migration events can occur towards the other demes under a migration model (e.g., the stepping-stone model [84]) (Fig. 1A). In addition to the migration rate, the number of emigrants and immigrants depend on the local and departure deme sizes, respectively. Intra-deme demography can be modelled by the population growth rate [e.g., 85]. A carrying capacity and friction (facility to move through) for each deme can also be considered to model the environmental conditions. All together the process can occur during a user-specified time (or number of generations), and at the end the landscape may become colonized (Fig. 1A).

Fig. (1).

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Spatially explicit simulation of a range expansion according to a 2D stepping-stone migration model [84] and posterior representation of the evolutionary history of a sample. A: The colonization of the lattice starts from the upper-left deme (source) that sends migrants to its neighboring demes. Colonized demes can send/receive individuals to/from the neighboring demes while non-colonized demes can only receive individuals. B: Evolutionary history of a random sample of 7 individuals collected at the present from different demes. Going backwards in time, these individuals can reach a most recent common ancestor (MRCA), which not necessarily (but often) belongs to the source deme.

Additional evolutionary processes of general interest, which can be simulated with spatially explicit models, are described in (Table 1).

Table 1.

Evolutionary processes of general interest that can be simulated with spatially explicit models.

Evolutionary Process Commentary References Population range expansion The colonization of a landscape through a spatial expansion is quite different from a pure demographic expansion and may generate particular genetic features such as sectors [107] and allele surfing (alleles riding on the wave of population range expansions [see, 143-145]). [81] Population range contractions and population range shifts Under hard living conditions (i.e., as a consequence of a climatic change or a invasive species) a population can reduce or shift its living range. [49] Heterogeneous environment and habitat fragmentation Habitats are frequently heterogeneous in the distribution of resources and as a consequence, they are not uniformly occupied. Indeed, habitats can be fragmented with spatial barriers leading to population fragmentation, which may result in loss of genetic diversity and sometimes may cause allopatric speciation [146-148]. [77, 117] Complex migration Species dispersal abilities can eventually determine the fate of the populations [146, 149] and should be carefully considered. Anisotropic migration (different migration rates towards the neighboring demes), sex-biased dispersal (i.e., induced by post-marital residence rules) or long-distance dispersal (LDD) may alter the colonization process and may influence genetic diversity. For instance, anisotropic migration towards refugia areas may lead to a larger loss of genetic diversity than isotropic migration [49]. LDD often increases genetic diversity [76, 77]. [49, 74, 76] Admixed populations Admixture between two populations may occur if both populations can interbreed [e.g., 113]. In this situations, demic diffusion can influence the spatial distribution of gene frequencies [21, 23]. [53, 74, 105] Open in a new tab

When applying computer simulations one should have in mind the role of each parameter on the entire evolutionary process. For example, the population size of a deme can increase with the population growth rate, the carrying capacity, and the number of immigrants. The number of emigrants from a deme depends on the migration rate and the population size. Thus, one can understand for example that a scenario with low carrying capacities (e.g., as a consequence of a climate change) and low migration rates may lead a population to extinction [e.g., 49, 86].

After the forward simulation, one could be interested in the evolutionary analysis of a particular sample. Here, some methods allow for the recovery of the history of a sample from the history of the entire population by using the coalescent (Fig. 1B) and then simulate genetic data for only such a sample [80, 85]. As expected, the main advantage of this procedure is the low computational cost for simulating genetic data. By contrast, other methods can simulate genetic data during the forward simulation leading to higher computational costs but allowing additional capabilities such as the ability to follow multi-locus genotypes within individuals or the analysis of all individuals of a deme [74].

SPATIALLY EXPLICIT EVOLUTIONARY FRAMEWORKS APPLIED IN HUMAN EVOLUTION

The implementation of spatially explicit models in available evolutionary frameworks is a recent development and is becoming increasingly more common with time. To date several spatially explicit computer simulators exist and implement different capabilities. (Table 2) shows a list of current spatially explicit simulators. These simulators can be classified as individual-based or deme-based population modeling (see Table 2). In theory, individual-based simulations can be more realistic than deme-based simulators but in practice, a similar performance was observed from both approaches [e.g., 74, 87].

Table 2.

The main publicly available evolutionary frameworks based on 2D spatially explicit models that can be applied to simulate human evolution. &#;Method&#; includes forward and coalescent approaches. &#;Category&#; indicates if the simulator is deme or individual-modeling oriented. &#;Scenario&#; indicates the implementation of the following evolutionary scenarios: de-mographics (D), population history and migration models (Pm), recombination or gene flow (R) and molecular adaptation or selection (S). &#;Genetic Marker&#; indicates the kind of genetic data that can be simulated, the implemented substitution models of evolution are described within a parenthesis. &#;Other capabilities&#; includes other interesting evolutionary fea-tures implemented in the simulator that may help generate more realistic simulations.

Program Method Category Scenario Genetic Marker Other Capabilities Reference Splatche/
Splatche2 Forward/coalescent Deme D, Pm, R DNA (JC, K2P)1, SNP, STR (SMM)2 and RFLP Long-distance dispersal
Anisotropic migration
Two populations and admixture [80, 85] KernelPop Forward Individual D, Pm STR (IAM, SMM)2, DNA (JC)1 Long-distance dispersal [150] IBDsim Forward/coalescent Individual D, Pm STR (IAM, KAM, GSM, SMM)2 - [151] CDPop Forward Individual D, Pm, S3 STR (KAM)2 Sex-biased migration and mating
Variable dispersal distance [130] EcoGenetics Forward Individual D, Pm STR (KAM, SMM)2 Sex-biased migration and mating Unpublished. See http://www2.unil.ch/
biomapper/ecogenetics/ Open in a new tab

Unfortunately, these simulators only implement a few substitution models and, for example, this limitation could generate unrealistic simulation of genome-wide data [e.g., 88-91]. Note that an incorrect substitution model (a model which does not fit well with the real data) may lead to &#;incorrect&#; simulations and derived estimations [e.g., 57, 92]. Other demanded capabilities can be the variation of demographic parameters with time (e.g., variable long-distance dispersal (LDD) rate and growth rate with time), covarion models of evolution [93], genomic rearrangement [94] and longitudinal sampling [95].

To our knowledge, only the spatially explicit available simulators SPLATCHE [85] and its new version SPLATCHE2 [80] have been applied in human evolution. This is probably due to their practical graphical user interface (GUI), the implementation of realistic evolutionary processes, and the simulation of genetic material evolution under a variety of genetic markers (see Table 1). Unfortunately, other spatially explicit simulators that have been applied to study human evolution are not publicly available. For example, in , Rendine et al. [96] developed a simple spatially-explicit tool to simulate a European Paleolithic and

Neolithic expansion with admixture (discussed in the following section). Recently, Rasteiro et al. [74] implemented a simulator similar to SPLATCHE which was individual-based and allowed for the consideration of sex-biased migration. Liu et al. [97] also developed a 1D spatially explicit simulator that was applied to simulate the world-wide human settlement. In the next section we describe several interesting applications of these simulators in human evolution.

HUMAN EVOLUTIONARY STUDIES BASED ON SPATIALLY EXPLICIT SIMULATIONS

The application of spatially explicit simulations in human evolution is becoming more popular with the passage of time. These realistic computer simulations are mainly applied in human evolution for comparing alternative models (i.e., geographic origins or different climate changes) and for estimating evolutionary parameters (i.e., rate of interbreeding in population admixtures). Below we describe some applications of spatially explicit simulations that can be of general interest.

• Geographic origin of early modern humans. As indicated in the Introduction, a geographic origin of early modern humans in Central or South Africa is commonly assumed [e.g., 6, 7] but there are other studies that suggest a North Africa origin [e.g., 9, 10] or even multiregional origins [11, 12]. These scenarios were evaluated by Ray et al. [98] through SPLATCHE simulations of the Old World human settlement. They performed simulations of a range expansion from 25 evenly distributed geographic origins. They also considered scenarios with a unique origin and multiregional origins (nine models based on different combinations of population sizes and migration rates between continents). Concerning the evolutionary parameters, they assumed an onset of the expansions of 120 kya, a generation time of 30 years according to [99], a growth rate of 0.3 [100], a migration rate of 0.05 (the number of emigrants is 5% of the population size), and a realistic carrying capacity for each deme (environmental heterogeneity). For each scenario, they simulated 10,000 samples of STR data for a total of 22 populations. Real samples from these 22 populations were collected from Rosenberg et al. [101] and this real data was applied to evaluate the different scenarios through statistical correlations. First results suggested a unique North African origin. Nevertheless, the consideration of ascertainment bias in the simulations suggested a unique East African origin. Liu et al. [97] also found this result by using worldwide spatially explicit computer simulations.

• Human genetic diversity gradients in Europe. Evidence for admixture between Paleolithic and Neolithic humans and past range contractions followed by re-expansions. The Paleolithic European colonization was dated between approximately 45 and 40 kya [102] and, as noted in the Introduction, it was initially studied by Cavalli-Sforza et al. [20] by applying PCA on spatially distributed allele frequencies. The resulting PC gradients presented a southeast (SE)-northwest (NW) axis [103, 104], and were interpreted to be a consequence of DD of Neolithic farmers that replaced Paleolithic hunter-gatherer populations with some admixture [21-23]. This interpretation was largely discussed. In , Currat and Excoffier [105] performed spatially explicit simulations of the colonization of Europe by pure Neolithic populations and by Paleolithic and Neolithic populations under different levels of admixture. They found that both Paleolithic and Neolithic populations resulted in SE-NW genetic diversity gradients as a consequence of allele surfing in the wave of the expansion, but not as a consequence of DD as suggested by Cavalli-Sforza et al. In , François et al. [106] repeated these simulations with updates in the evolutionary scenarios. They computed PC gradients from the simulated data. Counterintuitively, the resulting PC1 gradient presented a SW-NE orientation that is orthogonal to the range expansion (Fig. 2D, right). This PC1 gradient was explained as a consequence of allele surfing based on geographic sectors along the Neolithic wave of the expansion [107]. In , Arenas et al. [53] performed extensive simulations of more sophisticated evolutionary scenarios including range contractions (as a consequence of the LGM) towards Southern Europe (Fig. 2B, left) or towards the Iberian Peninsula (Fig. 2C, left) [see 49, 108]. These simulations included a refugial isolation period that was followed by a re-expansion. Pure Paleolithic expansion (Fig. 2A, left) and different admixture levels were also evaluated. This study showed that pure Paleolithic populations lead to a SE-NW PC1 gradient (Fig. 2A, right; similar to the gradients obtained by Cavalli-Sforza et al.) caused by a homogenization of molecular diversity [53]. By contrast, pure Neolithic populations resulted in a SW-NE PC1 gradient (Fig. 2D, right; similar to the gradients obtained by François et al.). In addition, PC1 gradients varied between those orientations as a function of the amount of admixture [53]. On the other hand, range contraction scenarios generated PC1 gradients orthogonal to the axis of the range re-expansion (Fig. 2B and 2C, right), especially for scenarios with higher Paleolithic contribution (note that the LGM occurs during the Paleolithic period). Overall, both the location of the refugia and the level of admixture influence PC gradients. The gradients by Cavalli-Sforza et al. are reproducible under a large Paleolithic contribution (Fig. 2A, right) or under a range contraction towards the Iberian Peninsula (Fig. 2C, right).

• Admixture between modern humans and Neanderthals. The admixture between modern humans and preexisting humans was studied by Currat and Excoffier [109]. They simulated mitochondrial DNA (mtDNA) data by spatially explicit simulations under a scenario where early modern humans colonize Europe with different amounts of admixture with Neanderthals. In case of admixture, massive introgression genes from Neanderthals to modern humans might have taken place during the invasion [110]. However, the authors found that the maximum possible contribution of Neanderthals into modern humans was smaller than 0.1%, suggesting almost complete sterility between modern humans and Neanderthals. More recently, these authors published a more sophisticated study on admixture in Eurasians [111]. In particular, they performed extensive spatially explicit simulations with variable amounts of admixture and under a variety of evolutionary scenarios based on different levels of migration rates, carrying capacities and growth rates. They then computed the maximum likelihood for each model and selected the best model through the Akaike information criterion (AIC) [112]. The results indicated a very low rate of interbreeding (smaller than 2%), suggesting an important barrier to gene flow between both species.

• Sex-biased migration during the Neolithic transition. An important feature in human evolution is the different demographic histories for males and females [113, 114]. For example, Hamilton et al. [87] performed spatially explicit simulations of human populations from northern Thailand [115] to show that &#;the number of male immigrants is much smaller (8 times) in patrilocal populations than in matrilocal populations&#;, and by contrast, &#;females move 2.5 times more in patrilocal populations than in matrilocal populations&#; [87]. Recently, Rasteiro et al. [74] studied the role of post-marital residence (PMR) and admixture between Paleolithic and Neolithic populations. They developed an individual-based spatially explicit simulator (not publicly available) that implements sex-biased migration. They then simulated scenarios where Neolithic populations colonize Europe under different amounts of admixture with Paleolithic populations, and under different patterns of PMR. To study the role of PMR, they simulated both mtDNA and Y-chromosome (NRY) data. Simulated datasets were evaluated with real data [38, 116] to select which model best fit the real information. The results indicated that patrilocality in farmers explained the genetic diversity better than matrilocality or bilocality. In addition, they observed that the genetic diversity of farmers can also be influenced by Paleolithic PMR rules.

Fig. (2).

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Illustrative examples of spatially explicit simulation of modern human colonization of Europe and principal component analysis derived from the simulated genetic diversity. Left: Snapshots of SPLATCHE2 to simulate an example of a: (A) Paleolithic range expansion over Europe; (B) Paleolithic range contraction towards Southern Europe and posterior re-expansion; (C) Paleolithic range contraction towards the Iberian Peninsula and posterior re-expansion; (D) Neolithic range expansion over Europe where the brown area is colonized by Paleolithic populations, the black area is colonized by Neolithic populations and the green region indicates a zone of cohabitation; at the end of this simulation Paleolithic populations are totally replaced by Neolithic populations. Settings (demographic parameter values) that we have applied to perform these simulations follow Arenas et al. [53]. The simulated population range expansions always start from the Middle East. Snapshots are taken each 50 generations. Right: Illustrative example of PC1 gradients for each above-described scenario. The black lines represent the PC1 gradient orientation, namely NW-SE for (A), W-E for (B), NW-SE for (C) and SW-NE for (D).

THE FUTURE OF SPATIALLY EXPLICIT COMPUTER SIMULATIONS IN HUMAN EVOLUTION

Spatially explicit models are fundamental to mimic the evolutionary history of terrestrial species because the consideration of spatio-temporal phenomena like range expansions, range contractions, range shifts, LDD, and habitat fragmentation, can influence genetic diversity [e.g., 49, 76, 77, 117]. Different evolutionary frameworks have been developed for the simulation of molecular data under spatially explicit models. However, these simulators implement very simple substitution models of evolution, and it is known that an assumed model, that is more simple than the true model, may lead to incorrect results [e.g., 57, 92]. Indeed, some of these simulators ignore recombination, which can bias evolutionary inferences [e.g., 118-121]. As a consequence, there is a need for more realistic computer simulators that implement complex substitution models of evolution, not only at the nucleotide level, but also at the codon [e.g., 43, 69], protein [e.g., 122-124] and genome-wide levels [88, 90]. Indeed, recombination (as well as other processes of exchange of genetic material) may generate evolutionary networks [67, 125] that should be considered to properly describe the history of human populations [see 18, 126].

As expected, most of the studies mentioned in the previous section could be improved with the consideration of additional evolutionary processes. For example, some available spatially explicit simulators implement LDD. However, this feature has not been applied yet in studies of human evolution. This fact could be explained by the complexity in the definition of the LDD, which includes priors for the LDD rate, dispersal distance, and direction of the dispersal events [see 76, 127] that should be studied from real observations [76]. Furthermore, other complex migration forms like anisotropic migration [49, 53] and sex-biased dispersal [e.g., 74, 87] can also influence genetic diversity and should always be considered. One could also expect that some of these evolutionary processes could vary with time. For example, population growth rates and dispersal distances could increase with time due to acculturation [105]. In addition, the topographic map and its resources could also change with time [e.g., 29]. As noted above, the LGM period could lead to past range contractions towards refugia areas [49, 53] and could allow the colonization of the Americas through the Bering Strait [28, 29]. Natural selection is another evolutionary force that should be considered to simulate human evolution [see 128, 129]. In spatially explicit simulations, to date only CDPop [130] implements natural selection [131, 132] and unfortunately, it was not applied to humans yet. All together, to obtain accurate and realistic results it is important to consider complex evolutionary models and model updates according with the simulated evolutionary time. Of course, more complex models do not necessarily lead to more realistic simulations, but if the complexity comes from real features and observations, such complex models should be taken into account.

On the other hand, robust inferences of human evolution will probably require the use of genome data. In this concern, next-generation sequencing (NGS) technologies now deliver fast and accurate genome sequences [133]. Note that complete and near-complete ancient human genomes are currently being obtained [e.g., 34, 134, 135]. However, the complexity of genome evolution [88, 89] may result in models and data where a likelihood function cannot be computed [136, 137]. As an alternative, analytical methods based on computer simulations are emerging since last years, in particular, the Bayesian model-choice [138] and the approximate Bayesian computation (ABC) approach [see for a review, 55, 139]. An additional goal of these methods is their ability to co-estimate evolutionary parameters. Since molecular evolution consists of the joint action of all the evolutionary processes together, ideally, one would want to estimate these parameters simultaneously to avoid potential biases [54, 56]. By contrast, these methods require extensive simulations and therefore, fast computer simulators are desired. For example, SPLATCHE2 has combined the forward and the coalescent methods to perform rapid simulations by multiple sampling of genetic data (coalescent) from a previously simulated entire population (forward-time). In addition, this program allows parallelization of the simulations on a cluster, which can alleviate computer times. Since last years, ABC is more frequently applied to the analysis of human evolution [e.g., 50, 87, 140-142]. Nevertheless, to our knowledge only the study by Hamilton et al. [87] applies an ABC method based on spatially explicit simulations. We believe the application of ABC in spatially explicit contexts will benefit future human evolutionary inferences.

Altogether, this review examines current methods and software applied to perform spatially explicit simulations of human evolution. We found that to date only a few simulators have been developed for this purpose and they still assume a number of evolutionary aspects (i.e. too simple substitution models of evolution and neutral evolution). Therefore, there is a continuous need for fast and more realistic spatially explicit simulators and we expect future advances in this concern. As a consequence, we also expect much more application of spatially explicit simulations in analysis of human evolution.

ACKNOWLEDGEMENTS

We want to thank the Editor of Current Genomics for the invitation to contribute with this review. We also want to thank Laurent Excoffier (as well as other members of the CMPG Lab), Mathias Currat, Nicolas Ray and Olivier François for fascinating discussions and studies during last years. We thank David A. Liberles, Russell A. Hermansen and Anke Konrad for helpful comments. We thank two reviewers for insightful comments and suggestions. M.A. was supported by the &#;Juan de la Cierva&#; fellowship JCI-- (Spanish Government) and the EMBO fellowship &#;ASTF 367-&#;.

CONFLICT OF INTEREST

The author(s) confirm that this article content has no conflict of interest.

Hypothesis that reality could be a computer simulation

The simulation hypothesis proposes that what sentient beings experience as the world is actually a simulated reality, such as a computer simulation in which the sentient beings themselves are constructs.[1][2] There has been much debate over this topic in the philosophical discourse, and regarding practical applications in computing. While the idea has become well known in popular culture, no substantial arguments or evidence exists as of yet in support of the idea having benefits, side-effects -- or indeed any implications -- for research in any field of scientific endevor.

In Konrad Zuse published his book Rechnender Raum (Calculating Space) on automata theory, where he proposes the idea that the universe is the result of computation. Based on the foundations of information- and computer science, this is the first documented modern version of the simulation hypothesis. In , philosopher Nick Bostrom proposed the simulation argument, which suggests that if a civilization becomes capable of creating conscious simulations, it could generate so many simulated beings that a randomly chosen conscious entity would almost certainly be in a simulation. The argument presents a trilemma: either such simulations are not created due to technological limitations or self-destruction; or advanced civilizations choose not to create them; or we are almost certainly living in one. This assumes that consciousness is not uniquely tied to biological brains but can arise from any system that implements the right computational structures and processes.[3][4]

The hypothesis is preceded by many earlier versions, and variations on the idea have also been featured in science fiction, appearing as a central plot device in many stories and films, such as The Matrix ().[5]

Origins

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Human history is full of thinkers who observed the difference between how things seem and how they might actually be, with dreams, illusions, and hallucinations providing poetic and philosophical metaphors. For example, the "Butterfly Dream" of Zhuangzi from ancient China,[6] or the Indian philosophy of Maya, or in ancient Greek philosophy&#;Anaxarchus and Monimus likened existing things to a scene-painting and supposed them to resemble the impressions experienced in sleep or madness.[7]

Aztec philosophical texts theorized that the world was a painting or book written by the Teotl.[8]

In the Western philosophical tradition, Plato's allegory of the cave, presented in the 4th century BCE, stands out as an influential example.

René Descartes' evil demon philosophically formalized these epistemic doubts, to be followed by a large literature with subsequent variations like brain in a vat.

Konrad Zuse's formulation, based on the foundation of information and computer science, is the first modern version of the simulation hypothesis.

Simulation argument

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Nick Bostrom in

Nick Bostrom's premise:

Many works of science fiction as well as some forecasts by serious technologists and futurologists predict that enormous amounts of computing power will be available in the future. Let us suppose for a moment that these predictions are correct. One thing that later generations might do with their super-powerful computers is run detailed simulations of their forebears or of people like their forebears. Because their computers would be so powerful, they could run a great many such simulations. Suppose that these simulated people are conscious (as they would be if the simulations were sufficiently fine-grained and if a certain quite widely accepted position in the philosophy of mind is correct). Then it could be the case that the vast majority of minds like ours do not belong to the original race but rather to people simulated by the advanced descendants of an original race.

Bostrom's conclusion:

It is then possible to argue that, if this were the case, we would be rational to think that we are likely among the simulated minds rather than among the original biological ones.
Therefore, if we don't think that we are currently living in a computer simulation, we are not entitled to believe that we will have descendants who will run lots of such simulations of their forebears.

Nick Bostrom, Are You Living in a Computer Simulation?,

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Expanded argument

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Bostrom attempted to assess the probability of our reality being a simulation.[10] His argument states that at least one of the following statements is very likely to be true:

1. Human civilization or a comparable civilization is unlikely to reach a level of technological maturity capable of producing simulated realities, or such simulations are physically impossible to construct.

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2. A comparable civilization reaching aforementioned technological status will likely not produce a significant number of simulated realities (one that might push the probable existence of digital entities beyond the probable number of "real" entities in a Universe) for any of a number of reasons, such as diversion of computational processing power for other tasks, ethical considerations of holding entities captive in simulated realities, etc.

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3. Any entities with our general set of experiences are almost certainly living in a simulation.

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4. Humans are living in a reality in which post-humans have not developed yet, and current humans are actually living in reality.

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5. Humans will have no way of knowing that they live in a simulation because they will never reach the technological capacity to realize the marks of a simulated reality.

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Bostrom's argument rests on the premise that given sufficiently advanced technology, it is possible to represent the populated surface of the Earth without recourse to digital physics; that the qualia experienced by a simulated consciousness are comparable or equivalent to those of a naturally occurring human consciousness, and that one or more levels of simulation within simulations would be feasible given only a modest expenditure of computational resources in the real world.[10]

First, if one assumes that humans will not be destroyed nor destroy themselves before developing such a technology, and that human descendants will have no overriding legal restrictions or moral compunctions against simulating biospheres or their own historical biosphere, then, Bostrom argues it would be unreasonable to count ourselves among the small minority of genuine organisms who, sooner or later, will be vastly outnumbered by artificial simulations.[10]

Epistemologically, it is not impossible for humans to tell whether they are living in a simulation. For example, Bostrom suggests that a window could pop up saying: "You are living in a simulation. Click here for more information". However, imperfections in a simulated environment might be difficult for the native inhabitants to identify and for purposes of authenticity, even the simulated memory of a blatant revelation might be purged programmatically. Nonetheless, should any evidence come to light, either for or against the skeptical hypothesis, it would radically alter the aforementioned probability.[10]

In , Bostrom proposed a trilemma that he called "the simulation argument". Despite its name, the "simulation argument" does not directly argue that humans live in a simulation; instead, it argues that one of three unlikely-seeming propositions is almost certainly true:

1. "The fraction of human-level civilizations that reach a posthuman stage (that is, one capable of running high-fidelity ancestor simulations) is very close to zero", or
2. "The fraction of posthuman civilizations that are interested in running simulations of their evolutionary history, or variations thereof, is very close to zero", or
3. "The fraction of all people with our kind of experiences that are living in a simulation is very close to one".

The trilemma points out that a technologically mature "posthuman" civilization would have enormous computing power; if even a tiny percentage of them were to run "ancestor simulations" (that is, "high-fidelity" simulations of ancestral life that would be indistinguishable from reality to the simulated ancestor), the total number of simulated ancestors, or "Sims", in the universe (or multiverse, if it exists) would greatly exceed the total number of actual ancestors.

Bostrom goes on to use a type of anthropic reasoning to claim that, if the third proposition is the one of those three that is true, and almost all people live in simulations, then humans are almost certainly living in a simulation.

Bostrom claims his argument goes beyond the classical ancient "skeptical hypothesis", claiming that "... we have interesting empirical reasons to believe that a certain disjunctive claim about the world is true", the third of the three disjunctive propositions being that humans are almost certainly living in a simulation. Thus, Bostrom, and writers in agreement with Bostrom such as David Chalmers, argue there might be empirical reasons for the "simulation hypothesis", and that therefore the simulation hypothesis is not a skeptical hypothesis but rather a "metaphysical hypothesis". Bostrom states he personally sees no strong argument as to which of the three trilemma propositions is the true one: "If (1) is true, then we will almost certainly go extinct before reaching posthumanity. If (2) is true, then there must be a strong convergence among the courses of advanced civilizations so that virtually none contains any individuals who desire to run ancestor-simulations and are free to do so. If (3) is true, then we almost certainly live in a simulation. In the dark forest of our current ignorance, it seems sensible to apportion one's credence roughly evenly between (1), (2), and (3)... I note that people who hear about the simulation argument often react by saying, 'Yes, I accept the argument, and it is obvious that it is possibility #n that obtains.' But different people pick a different n. Some think it obvious that (1) is true, others that (2) is true, yet others that (3) is true".

As a corollary to the trilemma, Bostrom states that "Unless we are now living in a simulation, our descendants will almost certainly never run an ancestor-simulation".[9][12][13][14]

Criticism of Bostrom's anthropic reasoning

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Bostrom argues that if "the fraction of all people with our kind of experiences that are living in a simulation is very close to one", then it follows that humans probably live in a simulation. Some philosophers disagree, proposing that perhaps "Sims" do not have conscious experiences the same way that unsimulated humans do, or that it can otherwise be self-evident to a human that they are a human rather than a Sim.[12][15] Philosopher Barry Dainton modifies Bostrom's trilemma by substituting "neural ancestor simulations" (ranging from literal brains in a vat, to far-future humans with induced high-fidelity hallucinations that they are their own distant ancestors) for Bostrom's "ancestor simulations", on the grounds that every philosophical school of thought can agree that sufficiently high-tech neural ancestor simulation experiences would be indistinguishable from non-simulated experiences. Even if high-fidelity computer Sims are never conscious, Dainton's reasoning leads to the following conclusion: either the fraction of human-level civilizations that reach a posthuman stage and are able and willing to run large numbers of neural ancestor simulations is close to zero, or some kind of (possibly neural) ancestor simulation exists.[16]

The hypothesis has received criticism from some physicists, such as Sabine Hossenfelder, who considers that it is physically impossible to simulate the universe without producing measurable inconsistencies, and called it pseudoscience and religion.[17] Cosmologist George F. R. Ellis, who stated that "[the hypothesis] is totally impracticable from a technical viewpoint", and that "late-night pub discussion is not a viable theory".[18][19] Some scholars categorically reject&#;or are uninterested in&#;anthropic reasoning, dismissing it as "merely philosophical", unfalsifiable, or inherently unscientific.[12]

Some critics propose that the simulation could be in the first generation, and all the simulated people that will one day be created do not yet exist,[12] in accordance with philosophical presentism.

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The cosmologist Sean M. Carroll argues that the simulation hypothesis leads to a contradiction: if humans are typical, as it is assumed, and not capable of performing simulations, this contradicts the arguer's assumption that it is easy for us to foresee that other civilizations can most likely perform simulations.[20]

Physicist Frank Wilczek raises an empirical objection, saying that the laws of the universe have hidden complexity which is "not used for anything" and the laws are constrained by time and location &#; all of this being unnecessary and extraneous in a simulation. He further argues that the simulation argument amounts to "begging the question," due to the "embarrassing question" of the nature of the underlying reality in which this universe is simulated. "Okay if this is a simulated world, what is the thing in which it is simulated made out of? What are the laws for that?"[21]

Brian Eggleston has argued that the future humans of our universe cannot be the ones performing the simulation, since the simulation argument considers our universe to be the one being simulated.[22] In other words, it has been argued that the probability that humans live in a simulated universe is not independent of the prior probability that is assigned to the existence of other universes.

Arguments, within the trilemma, against the simulation hypothesis

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Simulation down to molecular level of very small sample of matter

Some scholars accept the trilemma, and argue that the first or second of the propositions are true, and that the third proposition (the proposition that humans live in a simulation) is false. Physicist Paul Davies uses Bostrom's trilemma as part of one possible argument against a near-infinite multiverse. This argument runs as follows: if there were a near-infinite multiverse, there would be posthuman civilizations running ancestor simulations, which would lead to the untenable and scientifically self-defeating conclusion that humans live in a simulation; therefore, by reductio ad absurdum, existing multiverse theories are likely false. (Unlike Bostrom and Chalmers, Davies (among others) considers the simulation hypothesis to be self-defeating.)[12][23]

Some point out that there is currently no proof of technology that would facilitate the existence of sufficiently high-fidelity ancestor simulation. Additionally, there is no proof that it is physically possible or feasible for a posthuman civilization to create such a simulation, and therefore for the present, the first proposition must be taken to be true.[12] Additionally there are limits of computation.[9][24]

Physicist Marcelo Gleiser objects to the notion that posthumans would have a reason to run simulated universes: "...being so advanced they would have collected enough knowledge about their past to have little interest in this kind of simulation. ...They may have virtual-reality museums, where they could go and experience the lives and tribulations of their ancestors. But a full-fledged, resource-consuming simulation of an entire universe? Sounds like a colossal waste of time". Gleiser also points out that there is no plausible reason to stop at one level of simulation, so that the simulated ancestors might also be simulating their ancestors, and so on, creating an infinite regress akin to the "problem of the First Cause".[25]

In , philosopher Preston Greene suggested that it may be best not to find out if we are living in a simulation, since, if it were found to be true, such knowing might end the simulation.[26]

Greene's suggestion is similar to Douglas Adams' humorous idea presented in his novel The Hitchhiker's Guide to the Galaxy: that if anyone in the Universe should actually work out 'The Meaning of Life, the Universe and Everything', it would instantly disappear and be immediately replaced with something "even more complex and inexplicable".

Economist Robin Hanson argues that a self-interested occupant of a high-fidelity simulation should strive to be entertaining and praiseworthy in order to avoid being turned off or being shunted into a non-conscious low-fidelity part of the simulation. Hanson additionally speculates that someone who is aware that he might be in a simulation might care less about others and live more for today: "your motivation to save for retirement, or to help the poor in Ethiopia, might be muted by realizing that in your simulation, you will never retire and there is no Ethiopia".[27]

Besides attempting to assess whether the simulation hypothesis is true or false, philosophers have also used it to illustrate other philosophical problems, especially in metaphysics and epistemology. David Chalmers has argued that simulated beings might wonder whether their mental lives are governed by the physics of their environment, when in fact these mental lives are simulated separately (and are thus, in fact, not governed by the simulated physics).[28] Chalmers claims that they might eventually find that their thoughts fail to be physically caused, and argues that this means that Cartesian dualism is not necessarily as problematic of a philosophical view as is commonly supposed, though he does not endorse it.[29] Similar arguments have been made for philosophical views about personal identity that say that an individual could have been another human being in the past, as well as views about qualia that say that colors could have appeared differently than they do (the inverted spectrum scenario). In both cases, the claim is that all this would require is hooking up the mental lives to the simulated physics in a different way.[30]

Computationalism

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Computationalism is a philosophy of mind theory stating that cognition is a form of computation. It is relevant to the simulation hypothesis in that it illustrates how a simulation could contain conscious subjects, as required by a "virtual people" simulation. For example, it is well known that physical systems can be simulated to some degree of accuracy. If computationalism is correct and if there is no problem in generating artificial consciousness or cognition, it would establish the theoretical possibility of a simulated reality. Nevertheless, the relationship between cognition and phenomenal qualia of consciousness is disputed. It is possible that consciousness requires a vital substrate that a computer cannot provide and that simulated people, while behaving appropriately, would be philosophical zombies. This would undermine Nick Bostrom's simulation argument; humans cannot be a simulated consciousness, if consciousness, as humans understand it, cannot be simulated. The skeptical hypothesis remains intact, however, and humans could still be vatted brains, existing as conscious beings within a simulated environment, even if consciousness cannot be simulated. It has been suggested that whereas virtual reality would enable a participant to experience only three senses (sight, sound and optionally smell), simulated reality would enable all five (including taste and touch).[citation needed]

Some theorists[31][32] have argued that if the "consciousness-is-computation" version of computationalism and mathematical realism (or radical mathematical Platonism)[33] are true, then consciousness is computation, which in principle is platform independent and thus admits of simulation. This argument states that a "Platonic realm" or ultimate ensemble would contain every algorithm, including those that implement consciousness. Hans Moravec has explored the simulation hypothesis and has argued for a kind of mathematical Platonism according to which every object (including, for example, a stone) can be regarded as implementing every possible computation.[34]

In physics

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In physics, the view of the universe and its workings as the ebb and flow of information was first observed by Wheeler.[35] Consequently, two views of the world emerged: the first one proposes that the universe is a quantum computer,[36] while the other one proposes that the system performing the simulation is distinct from its simulation (the universe).[37] Of the former view, quantum-computing specialist Dave Bacon wrote:

In many respects this point of view may be nothing more than a result of the fact that the notion of computation is the disease of our age&#;everywhere we look today we see examples of computers, computation, and information theory and thus we extrapolate this to our laws of physics. Indeed, thinking about computing as arising from faulty components, it seems as if the abstraction that uses perfectly operating computers is unlikely to exist as anything but a platonic ideal. Another critique of such a point of view is that there is no evidence for the kind of digitization that characterizes computers nor are there any predictions made by those who advocate such a view that have been experimentally confirmed.[38]

Testing the hypothesis physically

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A method to test one type of simulation hypothesis was proposed in in a joint paper by physicists Silas R. Beane from the University of Bonn (now at the University of Washington, Seattle), and Zohreh Davoudi and Martin J. Savage from the University of Washington, Seattle.[39] Under the assumption of finite computational resources, the simulation of the universe would be performed by dividing the space-time continuum into a discrete set of points, which may result in observable effects. In analogy with the mini-simulations that lattice-gauge theorists run today to build up nuclei from the underlying theory of strong interactions (known as quantum chromodynamics), several observational consequences of a grid-like space-time have been studied in their work. Among proposed signatures is an anisotropy in the distribution of ultra-high-energy cosmic rays that, if observed, would be consistent with the simulation hypothesis according to these physicists.[40] In , Campbell et al. proposed several experiments aimed at testing the simulation hypothesis in their paper "On Testing the Simulation Theory".[41]

Advocates

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A high-profile proponent of the hypothesis is astrophysicist Neil Degrasse Tyson, who said in an NBC News interview that the hypothesis was correct, giving "better than 50&#;50 odds" and adding, "I wish I could summon a strong argument against it, but I can find none".[42] However, in a subsequent interview with Chuck Nice on a YouTube episode of StarTalk, Tyson shared that his friend J. Richard Gott, a professor of astrophysical sciences at Princeton University, made him aware of a strong objection to the simulation hypothesis. The objection claims that the common trait that all hypothetical high-fidelity simulated universes possess is the ability to produce high-fidelity simulated universes. And since our current world does not possess this ability, it would mean that either humans are in the real universe, and therefore simulated universes have not yet been created, or that humans are the last in a very long chain of simulated universes, an observation that makes the simulation hypothesis seem less probable. Regarding this objection, Tyson remarked "that changes my life".[43]

Rizwan Virk, of Massachusetts Institute of Technology is a founder of PlayLabs, and author of the novel, "The Simulation Hypothesis". A story about Virk trying on a virtual reality headset and forgetting he was in an empty room makes him wonder if the real world was created by more tech-savvy individuals, other than us.[44]

Other uses in philosophy

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Dream argument

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There is a long philosophical and scientific history to the underlying thesis that reality is an illusion. This skeptical hypothesis can be traced back to antiquity; for example, to the "Butterfly Dream" of Zhuangzi,[45] or the Indian philosophy of Maya, or in Ancient Greek philosophy Anaxarchus and Monimus likened existing things to a scene-painting and supposed them to resemble the impressions experienced in sleep or madness.[46]

A dream could be considered a type of simulation capable of fooling someone who is asleep. As a result, Bertrand Russell has argued that the "dream hypothesis" is not a logical impossibility, but that common sense as well as considerations of simplicity and inference to the best explanation rule against it.[47] One of the first philosophers to question the distinction between reality and dreams was Zhuangzi, a Chinese philosopher of the 4th century BC. He phrased the problem as the well-known "Butterfly Dream," which went as follows:

Once Zhuangzi dreamt he was a butterfly, a butterfly flitting and fluttering around, happy with himself and doing as he pleased. He didn't know he was Zhuangzi. Suddenly he woke up and there he was, solid and unmistakable Zhuangzi. But he didn't know if he was Zhuangzi who had dreamt he was a butterfly or a butterfly dreaming he was Zhuangzi. Between Zhuangzi and a butterfly there must be some distinction! This is called the Transformation of Things. (2, tr. Burton Watson :49)

The philosophical underpinnings of this argument are also brought up by Descartes, who was one of the first Western philosophers to do so. In Meditations on First Philosophy, he states "... there are no certain indications by which we may clearly distinguish wakefulness from sleep",[48] and goes on to conclude that "It is possible that I am dreaming right now and that all of my perceptions are false".[48]

Chalmers () discusses the dream hypothesis and notes that this comes in two distinct forms:

• that he is currently dreaming, in which case many of his beliefs about the world are incorrect;
• that he has always been dreaming, in which case the objects he perceives actually exist, albeit in his imagination.

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Both the dream argument and the simulation hypothesis can be regarded as skeptical hypotheses. Another state of mind in which some argue an individual's perceptions have no physical basis in the real world is psychosis, though psychosis may have a physical basis in the real world and explanations vary.

In On Certainty, the philosopher Ludwig Wittgenstein has argued that such skeptical hypothesis are unsinnig (i.e. non-sensical), as they doubt knowledge that is required in order to make sense of the hypotheses themselves.[50]

The dream hypothesis is also used to develop other philosophical concepts, such as Valberg's personal horizon: what this world would be internal to if this were all a dream.[51]

Lucid dreaming is characterized as an idea where the elements of dreaming and waking are combined to a point where the user knows they are dreaming, or waking perhaps.[52]

Modern philosophy

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A version of the simulation hypothesis was theorized as a part of a philosophical argument on the part of René Descartes, by George Berkeley (&#;) with his "immaterialism" (later referred to as subjective idealism by others)[citation needed], and later by Hans Moravec.[34][53][54]

• René Descartes (&#;) and his evil demon concept, sometimes also called his 'evil genius' concept

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• Aztec philosophical texts theorized that the world was a painting or book written by the Teotl.

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• Nietzsche, in Beyond Good and Evil chastised philosophers for seeking to find the true world behind the deceptive world of appearances.

It is nothing more than a moral prejudice that truth is worth more than semblance; it is, in fact, the worst proved supposition in the world.... Why might not the world which concerns us&#;&#;be a fiction?[57]

In popular culture

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Science fiction has highlighted themes such as virtual reality, artificial intelligence and computer gaming for more than fifty years.[58]

Simulacron-3 () by Daniel F. Galouye (alternative title: Counterfeit World) tells the story of a virtual city developed as a computer simulation for market research purposes, in which the simulated inhabitants possess consciousness; all but one of the inhabitants are unaware of the true nature of their world. The book was made into a German made-for-TV film called World on a Wire () directed by Rainer Werner Fassbinder and aired on ARD. The film The Thirteenth Floor () was also loosely based on both this book and World on a Wire. "We Can Remember It for You Wholesale" is a short story by American writer Philip K. Dick, first published in The Magazine of Fantasy & Science Fiction in April , and was the basis for the film Total Recall and its remake. In Overdrawn at the Memory Bank, a television film, the main character pays to have his mind connected to a simulation.[citation needed]

The same theme was repeated in the film The Matrix, which depicted a world in which artificially intelligent robots enslaved humanity within a simulation set in the contemporary world. The play World of Wires was partially inspired by the Bostrom essay on the simulation hypothesis.[59]

The visual novel Danganronpa 2: Goodbye Despair is set in a simulated reality known as the Neo World Program, which in this instance simulates a class trip to Jabberwock Island which, while initially peaceful, turns into a "killing game" involving the students in the simulation killing each other and trying to not be found guilty. Similarly, 's Anonymous;Code explores the idea of the world being a simulation, with an infinite or near-infinite number of "world layers" of simulations running inside other simulations. The main problem with this system is that in some of these "world layers", both above and below the one the characters find themselves living in, the Year Problem has not been solved, dooming the world to end on January 19, at 3:14:07 am UTC. The characters have to hack all the way into the highest world layer, the real world that the player lives in, to synchronize all the world layers and solve the Year problem in all of them.

The episode of the animated sitcom Rick and Morty, "M. Night Shaym-Aliens!", demonstrates a low-quality simulation that attempts to trap the two titular protagonists, but because the operation is less "realistic" than typically operated "reality", it becomes obvious.

In , Kent Forbes published a documentary named "The Simulation Hypothesis", notably featuring Max Tegmark, Neil degrasse Tyson, Paul Davies and James Gates.[60]

In the video game No Man's Sky, the universe is a simulated universe run by The Atlas. According to in-game lore, many vastly different iterations of the universe existed, with very different histories and races. As the Atlas AI became more and more corrupted, the universes became more and more similar to each other.

A episode of the long-running British science fiction series Doctor Who titled "Extremis" features a simulated version of the Twelfth Doctor and his companions. A secret Vatican document describes the truth about the simulated reality by inviting its reader to choose any series of numbers at random. The document lists the same numbers on the next page since the simulated program cannot produce a truly random event. The simulation is finally revealed to be a practice world for aliens intent on real-world domination.

The Netflix epic period mystery-science fiction created by Jantje Friese and Baran bo Odar tells the unfinished story of a simulation scenario in which multiple persons find themselves in a circumstance of multiplicities and simultaneities. The storyline involves an amnesia, seemingly to protect the integrity of the simulation, as suggested would be necessary by the philosopher Preston Green.[26]

See also

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Notes

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References

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Further reading

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• "Are We Living in a Simulation?" BBC Focus magazine, March , pages 43&#;45. Interview with physicist Silas Beane of the University of Bonn discussing a proposed test for simulated reality evidence. Three pages, three photos, including one of Beane and a computer-generated scene from the film The Matrix. Publisher: Immediate Media Company, Bristol, UK.
• Conitzer, Vincent. "A Puzzle About Further Facts". Open access version of article in Erkenntnis.
• Lev, Gid'on. Life in the Matrix. Haaretz Magazine, April 25, , page 6.
• Merali, Zeeya. "Do We Live in the Matrix?" Discover, December , pages 24&#;25. Subtitle: "Physicists have proposed tests to reveal whether we are part of a giant computer simulation".

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