diff --git a/examples/no_strong_duality.jl b/examples/no_strong_duality.jl
new file mode 100644
index 000000000..819fa56fd
--- /dev/null
+++ b/examples/no_strong_duality.jl
@@ -0,0 +1,27 @@
+#  Copyright 2017-19, Oscar Dowson.
+#  This Source Code Form is subject to the terms of the Mozilla Public
+#  License, v. 2.0. If a copy of the MPL was not distributed with this
+#  file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+using SDDP, GLPK, Test
+
+function no_strong_duality()
+    model = SDDP.PolicyGraph(
+            SDDP.Graph(
+                :root,
+                [:node],
+                [(:root => :node, 1.0), (:node => :node, 0.5)]
+            ),
+            optimizer = with_optimizer(GLPK.Optimizer),
+            lower_bound = 0.0) do sp, t
+        @variable(sp, x, SDDP.State, initial_value = 1.0)
+        @stageobjective(sp, x.out)
+        @constraint(sp, x.in == x.out)
+    end
+
+    SDDP.train(model, iteration_limit = 20)
+
+    @test SDDP.calculate_bound(model) ≈ 2.0 atol=1e-8
+end
+
+no_strong_duality()
diff --git a/src/algorithm.jl b/src/algorithm.jl
index 223cf62d0..35b91f2f7 100644
--- a/src/algorithm.jl
+++ b/src/algorithm.jl
@@ -936,14 +936,14 @@ end
 # Internal function: helper to conduct a single simulation. Users should use the
 # documented, user-facing function SDDP.simulate instead.
 function _simulate(model::PolicyGraph{T},
-                   variables::Vector{Symbol} = Symbol[];
-                   sampling_scheme::AbstractSamplingScheme =
-                       InSampleMonteCarlo(),
-                   custom_recorders = Dict{Symbol, Function}()) where {T}
+                   variables::Vector{Symbol};
+                   sampling_scheme::AbstractSamplingScheme,
+                   custom_recorders::Dict{Symbol, Function},
+                   require_duals::Bool) where {T}
     # Sample a scenario path.
     scenario_path, terminated_due_to_cycle = sample_scenario(
-        model, sampling_scheme
-    )
+        model, sampling_scheme)
+
     # Storage for the simulation results.
     simulation = Dict{Symbol, Any}[]
     # The incoming state values.
@@ -1024,7 +1024,8 @@ end
              variables::Vector{Symbol} = Symbol[];
              sampling_scheme::AbstractSamplingScheme =
                  InSampleMonteCarlo(),
-             custom_recorders = Dict{Symbol, Function}()
+             custom_recorders = Dict{Symbol, Function}(),
+             require_duals::Bool = true
      )::Vector{Vector{Dict{Symbol, Any}}}
 
 Perform a simulation of the policy model with `number_replications` replications
@@ -1067,17 +1068,19 @@ The value of the dual in the first stage of the second replication can be
 accessed as:
 
     simulation_results[2][1][:constraint_dual]
+
+If you do not require dual variables (or if they are not available), pass
+`require_duals = false`.
 """
 function simulate(model::PolicyGraph,
                   number_replications::Int = 1,
                   variables::Vector{Symbol} = Symbol[];
                   sampling_scheme::AbstractSamplingScheme =
                       InSampleMonteCarlo(),
-                  custom_recorders = Dict{Symbol, Function}())
-    return [_simulate(
-                model,
-                variables;
-                sampling_scheme = sampling_scheme,
-                custom_recorders = custom_recorders)
-                for i in 1:number_replications]
+                  custom_recorders = Dict{Symbol, Function}(),
+                  require_duals::Bool = true)
+    return map(i -> _simulate(
+            model, variables; sampling_scheme = sampling_scheme,
+            custom_recorders = custom_recorders, require_duals = require_duals),
+        1:number_replications)
 end