simulate_hindex {hindex}

Simulate h-index and h-alpha values

Description

Simulate the effect of publishing, being cited, and (strategic) collaborating on the development of h-index and h-alpha values for a specified set of agents.

Usage

simulate_hindex(
  runs = 1,
  n = 100,
  periods = 20,
  subgroups_distr = 1,
  subgroup_advantage = 1,
  subgroup_exchange = 0,
  init_type = "fixage",
  distr_initial_papers = "poisson",
  max_age_scientists = 5,
  dpapers_pois_lambda = 2,
  dpapers_nbinom_dispersion = 1.1,
  dpapers_nbinom_mean = 2,
  productivity = 80,
  distr_citations = "poisson",
  dcitations_speed = 2,
  dcitations_peak = 3,
  dcitations_mean = 2,
  dcitations_dispersion = 1.1,
  coauthors = 5,
  strategic_teams = FALSE,
  diligence_share = 1,
  diligence_corr = 0,
  selfcitations = FALSE,
  update_alpha_authors = FALSE,
  boost = FALSE,
  boost_size = 0.1,
  alpha_share = 0.33
)

Arguments

runs

Number of times the simulation is repeated.

n

Number of agents acting in each simulation.

periods

Number of periods the agents collaborate across in each period.

subgroups_distr

Share of scientists in the first subgroup among all scientists

subgroup_advantage

Factor by which citations of papers published by agents of subgroup 2 exceed those of papers published by subgroup 1. This option is intended to reflect subdisciplines with different citation levels.

subgroup_exchange

Share of agents publishing (alone or in collaboration) with the other subgroup in each period. For example, when specifying subgroup_exchange = .1, 10% of each subgroup join the other subgroup each period.

init_type

Type of the initial setup. May be 'fixage' or 'varage'. For init_type = 'fixage', all initial papers have the same age (specified by max_age_scientists). For init_type = 'varage', papers get a random age which is less than or equal to max_age_scientists.

distr_initial_papers

Distribution of the papers the scientists have already published at the start of the simulation. Currently, the poisson distribution ("poisson") and the negative binomial distribution ("nbinomial") are supported.

max_age_scientists

Maximum age of scientists at the start of the simulation. For init_type = varage, a random age less than or equal to max_age_scientists is assigned to the initial papers. For init_type = fixage, all papers are max_age_scientists old.

dpapers_pois_lambda

The distribution parameter for a poisson distribution of initial papers.

dpapers_nbinom_dispersion

Dispersion parameter of a negative binomial distribution of initial papers.

dpapers_nbinom_mean

Expected value of a negative binomial distribution of initial papers.

productivity

The share of papers published by the 20% most productive agents in percentage. This parameter is only used for init_type = 'varage'. For init_type = 'fixage', diligence_share and diligence_corr can be used to control the productivity of scientists.

distr_citations

Distribution of citations the papers get. The expected value of this distribution follows a log-logistic function of time. Currently, the poisson distribution ("poisson") and the negative binomial distribution ("nbinomial") are supported.

dcitations_speed

The steepness (shape parameter) of the log-logistic time function of the expected citation values.

dcitations_peak

The period after publishing when the expected value of the citation distribution reaches its maximum.

dcitations_mean

The maximum expected value of the citation distribution (at period dcitations_peak after publishing, the citation distribution has dcitations_mean).

dcitations_dispersion

For a negative binomial citation distribution, dcitations_dispersion is a factor by which the variance exceeds the expected value.

coauthors

Average number of coauthors publishing papers.

strategic_teams

If this parameter is set to TRUE, agents with high h-index avoid co-authorships with agents who have equal or higher h-index values (they strategically select co-authors to improve their h-alpha index). This is implemented by assigning the agents with the highest h-index values to separate teams and randomly assigning the other agents to the teams. Otherwise, the collaborating agents are assigned to co-authorships at random.

diligence_share

The share of agents publishing in each period. Only used for init_type = 'fixage'.

diligence_corr

The correlation between the initial h-index value and the probability to publish in a given period. This parameter only has an effect if diligence_share < 1. Only used for init_type = 'fixage'.

selfcitations

If this parameter is set to TRUE, a paper gets one additional citation if at least one of its authors has a h-index value that exceeds the number of previous citations of the paper by one or two. This reflects agents strategically citing their own papers with citations just below their h-index to accelerate the growth of their h-index.

update_alpha_authors

If this parameter is set to TRUE, the alpha author of newly written papers is determined every period based on the current h-index values of its authors. Without this option, the alpha author is determined when the paper is written and held constant from then on.

boost

If this parameter is set to TRUE, papers of agents with a higher h-index are cited more frequently than papers of agents with lower h-index. For each team, this effect is based on the team's co-author with the highest h-index within this team.

boost_size

Magnitude of the boost effect. For every additional h point of a paper's co-author who has the highest h-index among all of the paper's co-authors, citations of the paper are increased by boost_size, rounded to the next integer.

alpha_share

The share of previously published papers where the corresponding agent is alpha author.

Value

For each run, the h-index values and the h-alpha values for each period are stored in a list of lists.

Examples

set.seed(123)
simdata <- simulate_hindex(runs = 2, n = 20, periods = 3)
plot_hsim(simdata, plot_hindex = TRUE)