diff --git a/examples/epstein_civil_violence/epstein_civil_violence/epstein_civil_violence.py b/examples/epstein_civil_violence/epstein_civil_violence/epstein_civil_violence.py
new file mode 100644
index 00000000..9a7314e0
--- /dev/null
+++ b/examples/epstein_civil_violence/epstein_civil_violence/epstein_civil_violence.py
@@ -0,0 +1,273 @@
+import enum
+import math
+
+from mesa import Agent, Model
+from mesa.experimental.devs.simulator import ABMSimulator
+from mesa.space import SingleGrid
+
+
+class EpsteinAgent(Agent):
+    def __init__(self, unique_id, model, vision, movement):
+        super().__init__(unique_id, model)
+        self.vision = vision
+        self.movement = movement
+
+
+class AgentState(enum.IntEnum):
+    QUIESCENT = enum.auto()
+    ARRESTED = enum.auto()
+    ACTIVE = enum.auto()
+
+
+class Citizen(EpsteinAgent):
+    """
+    A member of the general population, may or may not be in active rebellion.
+    Summary of rule: If grievance - risk > threshold, rebel.
+
+    Attributes:
+        unique_id: unique int
+        model :
+        hardship: Agent's 'perceived hardship (i.e., physical or economic
+            privation).' Exogenous, drawn from U(0,1).
+        regime_legitimacy: Agent's perception of regime legitimacy, equal
+            across agents.  Exogenous.
+        risk_aversion: Exogenous, drawn from U(0,1).
+        threshold: if (grievance - (risk_aversion * arrest_probability)) >
+            threshold, go/remain Active
+        vision: number of cells in each direction (N, S, E and W) that agent
+            can inspect
+        condition: Can be "Quiescent" or "Active;" deterministic function of
+            greivance, perceived risk, and
+        grievance: deterministic function of hardship and regime_legitimacy;
+            how aggrieved is agent at the regime?
+        arrest_probability: agent's assessment of arrest probability, given
+            rebellion
+    """
+
+    def __init__(
+        self,
+        unique_id,
+        model,
+        vision,
+        movement,
+        hardship,
+        regime_legitimacy,
+        risk_aversion,
+        threshold,
+        arrest_prob_constant,
+    ):
+        """
+        Create a new Citizen.
+        Args:
+            unique_id: unique int
+            model : model instance
+            hardship: Agent's 'perceived hardship (i.e., physical or economic
+                privation).' Exogenous, drawn from U(0,1).
+            regime_legitimacy: Agent's perception of regime legitimacy, equal
+                across agents.  Exogenous.
+            risk_aversion: Exogenous, drawn from U(0,1).
+            threshold: if (grievance - (risk_aversion * arrest_probability)) >
+                threshold, go/remain Active
+            vision: number of cells in each direction (N, S, E and W) that
+                agent can inspect. Exogenous.
+        """
+        super().__init__(unique_id, model, vision, movement)
+        self.hardship = hardship
+        self.regime_legitimacy = regime_legitimacy
+        self.risk_aversion = risk_aversion
+        self.threshold = threshold
+        self.condition = AgentState.QUIESCENT
+        self.grievance = self.hardship * (1 - self.regime_legitimacy)
+        self.arrest_probability = None
+        self.arrest_prob_constant = arrest_prob_constant
+
+    def step(self):
+        """
+        Decide whether to activate, then move if applicable.
+        """
+        self.update_neighbors()
+        self.update_estimated_arrest_probability()
+        net_risk = self.risk_aversion * self.arrest_probability
+        if self.grievance - net_risk > self.threshold:
+            self.condition = AgentState.ACTIVE
+        else:
+            self.condition = AgentState.QUIESCENT
+        if self.movement and self.empty_neighbors:
+            new_pos = self.random.choice(self.empty_neighbors)
+            self.model.grid.move_agent(self, new_pos)
+
+    def update_neighbors(self):
+        """
+        Look around and see who my neighbors are
+        """
+        self.neighborhood = self.model.grid.get_neighborhood(
+            self.pos, moore=True, radius=self.vision
+        )
+        self.neighbors = self.model.grid.get_cell_list_contents(self.neighborhood)
+        self.empty_neighbors = [
+            c for c in self.neighborhood if self.model.grid.is_cell_empty(c)
+        ]
+
+    def update_estimated_arrest_probability(self):
+        """
+        Based on the ratio of cops to actives in my neighborhood, estimate the
+        p(Arrest | I go active).
+        """
+        cops_in_vision = len([c for c in self.neighbors if isinstance(c, Cop)])
+        actives_in_vision = 1.0  # citizen counts herself
+        for c in self.neighbors:
+            if isinstance(c, Citizen) and c.condition == AgentState.ACTIVE:
+                actives_in_vision += 1
+        self.arrest_probability = 1 - math.exp(
+            -1 * self.arrest_prob_constant * (cops_in_vision / actives_in_vision)
+        )
+
+    def sent_to_jail(self, value):
+        self.model.active_agents.remove(self)
+        self.condition = AgentState.ARRESTED
+        self.model.simulator.schedule_event_relative(self.release_from_jail, value)
+
+    def release_from_jail(self):
+        self.model.active_agents.add(self)
+        self.condition = AgentState.QUIESCENT
+
+
+class Cop(EpsteinAgent):
+    """
+    A cop for life.  No defection.
+    Summary of rule: Inspect local vision and arrest a random active agent.
+
+    Attributes:
+        unique_id: unique int
+        x, y: Grid coordinates
+        vision: number of cells in each direction (N, S, E and W) that cop is
+            able to inspect
+    """
+
+    def __init__(self, unique_id, model, vision, movement, max_jail_term):
+        super().__init__(unique_id, model, vision, movement)
+        self.max_jail_term = max_jail_term
+
+    def step(self):
+        """
+        Inspect local vision and arrest a random active agent. Move if
+        applicable.
+        """
+        self.update_neighbors()
+        active_neighbors = []
+        for agent in self.neighbors:
+            if isinstance(agent, Citizen) and agent.condition == "Active":
+                active_neighbors.append(agent)
+        if active_neighbors:
+            arrestee = self.random.choice(active_neighbors)
+            arrestee.sent_to_jail(self.random.randint(0, self.max_jail_term))
+        if self.movement and self.empty_neighbors:
+            new_pos = self.random.choice(self.empty_neighbors)
+            self.model.grid.move_agent(self, new_pos)
+
+    def update_neighbors(self):
+        """
+        Look around and see who my neighbors are.
+        """
+        self.neighborhood = self.model.grid.get_neighborhood(
+            self.pos, moore=True, radius=self.vision
+        )
+        self.neighbors = self.model.grid.get_cell_list_contents(self.neighborhood)
+        self.empty_neighbors = [
+            c for c in self.neighborhood if self.model.grid.is_cell_empty(c)
+        ]
+
+
+class EpsteinCivilViolence(Model):
+    """
+    Model 1 from "Modeling civil violence: An agent-based computational
+    approach," by Joshua Epstein.
+    http://www.pnas.org/content/99/suppl_3/7243.full
+    Attributes:
+        height: grid height
+        width: grid width
+        citizen_density: approximate % of cells occupied by citizens.
+        cop_density: approximate % of cells occupied by cops.
+        citizen_vision: number of cells in each direction (N, S, E and W) that
+            citizen can inspect
+        cop_vision: number of cells in each direction (N, S, E and W) that cop
+            can inspect
+        legitimacy:  (L) citizens' perception of regime legitimacy, equal
+            across all citizens
+        max_jail_term: (J_max)
+        active_threshold: if (grievance - (risk_aversion * arrest_probability))
+            > threshold, citizen rebels
+        arrest_prob_constant: set to ensure agents make plausible arrest
+            probability estimates
+        movement: binary, whether agents try to move at step end
+        max_iters: model may not have a natural stopping point, so we set a
+            max.
+    """
+
+    def __init__(
+        self,
+        width=40,
+        height=40,
+        citizen_density=0.7,
+        cop_density=0.074,
+        citizen_vision=7,
+        cop_vision=7,
+        legitimacy=0.8,
+        max_jail_term=1000,
+        active_threshold=0.1,
+        arrest_prob_constant=2.3,
+        movement=True,
+        max_iters=1000,
+        seed=None,
+    ):
+        super().__init__(seed)
+        if cop_density + citizen_density > 1:
+            raise ValueError("Cop density + citizen density must be less than 1")
+
+        self.width = width
+        self.height = height
+        self.citizen_density = citizen_density
+        self.cop_density = cop_density
+
+        self.max_iters = max_iters
+
+        self.grid = SingleGrid(self.width, self.height, torus=True)
+
+        for _, pos in self.grid.coord_iter():
+            if self.random.random() < self.cop_density:
+                agent = Cop(
+                    self.next_id(),
+                    self,
+                    cop_vision,
+                    movement,
+                    max_jail_term,
+                )
+            elif self.random.random() < (self.cop_density + self.citizen_density):
+                agent = Citizen(
+                    self.next_id(),
+                    self,
+                    citizen_vision,
+                    movement,
+                    hardship=self.random.random(),
+                    regime_legitimacy=legitimacy,
+                    risk_aversion=self.random.random(),
+                    threshold=active_threshold,
+                    arrest_prob_constant=arrest_prob_constant,
+                )
+            else:
+                continue
+            self.grid.place_agent(agent, pos)
+
+        self.active_agents = self.agents
+
+    def step(self):
+        self.active_agents.shuffle(inplace=True).do("step")
+
+
+if __name__ == "__main__":
+    model = EpsteinCivilViolence(seed=15)
+    simulator = ABMSimulator()
+
+    simulator.setup(model)
+
+    simulator.run(time_delta=100)
diff --git a/examples/wolf_sheep/wolf_sheep.py b/examples/wolf_sheep/wolf_sheep.py
new file mode 100644
index 00000000..9fa6b3d9
--- /dev/null
+++ b/examples/wolf_sheep/wolf_sheep.py
@@ -0,0 +1,250 @@
+"""
+Wolf-Sheep Predation Model
+================================
+
+Replication of the model found in NetLogo:
+    Wilensky, U. (1997). NetLogo Wolf Sheep Predation model.
+    http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation.
+    Center for Connected Learning and Computer-Based Modeling,
+    Northwestern University, Evanston, IL.
+"""
+
+import mesa
+from mesa.experimental.devs.simulator import ABMSimulator
+
+
+class Animal(mesa.Agent):
+    def __init__(self, unique_id, model, moore, energy, p_reproduce, energy_from_food):
+        super().__init__(unique_id, model)
+        self.energy = energy
+        self.p_reproduce = p_reproduce
+        self.energy_from_food = energy_from_food
+        self.moore = moore
+
+    def random_move(self):
+        next_moves = self.model.grid.get_neighborhood(self.pos, self.moore, True)
+        next_move = self.random.choice(next_moves)
+        # Now move:
+        self.model.grid.move_agent(self, next_move)
+
+    def spawn_offspring(self):
+        self.energy /= 2
+        offspring = self.__class__(
+            self.model.next_id(),
+            self.model,
+            self.moore,
+            self.energy,
+            self.p_reproduce,
+            self.energy_from_food,
+        )
+        self.model.grid.place_agent(offspring, self.pos)
+
+    def feed(self): ...
+
+    def die(self):
+        self.model.grid.remove_agent(self)
+        self.remove()
+
+    def step(self):
+        self.random_move()
+        self.energy -= 1
+
+        self.feed()
+
+        if self.energy < 0:
+            self.die()
+        elif self.random.random() < self.p_reproduce:
+            self.spawn_offspring()
+
+
+class Sheep(Animal):
+    """
+    A sheep that walks around, reproduces (asexually) and gets eaten.
+
+    The init is the same as the RandomWalker.
+    """
+
+    def feed(self):
+        # If there is grass available, eat it
+        agents = self.model.grid.get_cell_list_contents(self.pos)
+        grass_patch = next(obj for obj in agents if isinstance(obj, GrassPatch))
+        if grass_patch.fully_grown:
+            self.energy += self.energy_from_food
+            grass_patch.fully_grown = False
+
+
+class Wolf(Animal):
+    """
+    A wolf that walks around, reproduces (asexually) and eats sheep.
+    """
+
+    def feed(self):
+        agents = self.model.grid.get_cell_list_contents(self.pos)
+        sheep = [obj for obj in agents if isinstance(obj, Sheep)]
+        if len(sheep) > 0:
+            sheep_to_eat = self.random.choice(sheep)
+            self.energy += self.energy
+
+            # Kill the sheep
+            sheep_to_eat.die()
+
+
+class GrassPatch(mesa.Agent):
+    """
+    A patch of grass that grows at a fixed rate and it is eaten by sheep
+    """
+
+    @property
+    def fully_grown(self) -> bool:
+        return self._fully_grown
+
+    @fully_grown.setter
+    def fully_grown(self, value: bool):
+        self._fully_grown = value
+
+        if not value:
+            self.model.simulator.schedule_event_relative(
+                setattr,
+                self.grass_regrowth_time,
+                function_args=[self, "fully_grown", True],
+            )
+
+    def __init__(self, unique_id, model, fully_grown, countdown, grass_regrowth_time):
+        """
+        Creates a new patch of grass
+
+        Args:
+            grown: (boolean) Whether the patch of grass is fully grown or not
+            countdown: Time for the patch of grass to be fully grown again
+        """
+        super().__init__(unique_id, model)
+        self._fully_grown = fully_grown
+        self.grass_regrowth_time = grass_regrowth_time
+
+        if not self.fully_grown:
+            self.model.simulator.schedule_event_relative(
+                setattr, countdown, function_args=[self, "fully_grown", True]
+            )
+
+    def set_fully_grown(self):
+        self.fully_grown = True
+
+
+class WolfSheep(mesa.Model):
+    """
+    Wolf-Sheep Predation Model
+
+    A model for simulating wolf and sheep (predator-prey) ecosystem modelling.
+    """
+
+    def __init__(
+        self,
+        height,
+        width,
+        initial_sheep,
+        initial_wolves,
+        sheep_reproduce,
+        wolf_reproduce,
+        grass_regrowth_time,
+        wolf_gain_from_food=13,
+        sheep_gain_from_food=5,
+        moore=False,
+        simulator=None,
+        seed=None,
+    ):
+        """
+        Create a new Wolf-Sheep model with the given parameters.
+
+        Args:
+            initial_sheep: Number of sheep to start with
+            initial_wolves: Number of wolves to start with
+            sheep_reproduce: Probability of each sheep reproducing each step
+            wolf_reproduce: Probability of each wolf reproducing each step
+            wolf_gain_from_food: Energy a wolf gains from eating a sheep
+            grass: Whether to have the sheep eat grass for energy
+            grass_regrowth_time: How long it takes for a grass patch to regrow
+                                 once it is eaten
+            sheep_gain_from_food: Energy sheep gain from grass, if enabled.
+            moore:
+        """
+        super().__init__(seed=seed)
+        # Set parameters
+        self.height = height
+        self.width = width
+        self.initial_sheep = initial_sheep
+        self.initial_wolves = initial_wolves
+        self.simulator = simulator
+
+        # self.sheep_reproduce = sheep_reproduce
+        # self.wolf_reproduce = wolf_reproduce
+        # self.grass_regrowth_time = grass_regrowth_time
+        # self.wolf_gain_from_food = wolf_gain_from_food
+        # self.sheep_gain_from_food = sheep_gain_from_food
+        # self.moore = moore
+
+        self.grid = mesa.space.MultiGrid(self.height, self.width, torus=False)
+
+        for _ in range(self.initial_sheep):
+            pos = (
+                self.random.randrange(self.width),
+                self.random.randrange(self.height),
+            )
+            energy = self.random.randrange(2 * sheep_gain_from_food)
+            sheep = Sheep(
+                self.next_id(),
+                self,
+                moore,
+                energy,
+                sheep_reproduce,
+                sheep_gain_from_food,
+            )
+            self.grid.place_agent(sheep, pos)
+
+        # Create wolves
+        for _ in range(self.initial_wolves):
+            pos = (
+                self.random.randrange(self.width),
+                self.random.randrange(self.height),
+            )
+            energy = self.random.randrange(2 * wolf_gain_from_food)
+            wolf = Wolf(
+                self.next_id(),
+                self,
+                moore,
+                energy,
+                wolf_reproduce,
+                wolf_gain_from_food,
+            )
+            self.grid.place_agent(wolf, pos)
+
+        # Create grass patches
+        possibly_fully_grown = [True, False]
+        for _agent, pos in self.grid.coord_iter():
+            fully_grown = self.random.choice(possibly_fully_grown)
+            if fully_grown:
+                countdown = grass_regrowth_time
+            else:
+                countdown = self.random.randrange(grass_regrowth_time)
+            patch = GrassPatch(
+                self.next_id(), self, fully_grown, countdown, grass_regrowth_time
+            )
+            self.grid.place_agent(patch, pos)
+
+    def step(self):
+        self.get_agents_of_type(Sheep).shuffle(inplace=True).do("step")
+        self.get_agents_of_type(Wolf).shuffle(inplace=True).do("step")
+
+
+if __name__ == "__main__":
+    import time
+
+    simulator = ABMSimulator()
+
+    model = WolfSheep(25, 25, 60, 40, 0.2, 0.1, 20, simulator=simulator, seed=15)
+
+    simulator.setup(model)
+
+    start_time = time.perf_counter()
+    simulator.run(100)
+    print(simulator.time)
+    print("Time:", time.perf_counter() - start_time)